docs/source/notebooks/clv/dev/pareto_nbd_advi.ipynb

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   "source": [
    "import arviz as az \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pymc as pm\n",
    "import pymc_extras as pmx\n",
    "import seaborn as sns\n",
    "\n",
    "from pymc_marketing import clv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
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    }
   },
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_cdnow = \"https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/data/cdnow_transactions.csv\"\n",
    "\n",
    "raw_data = pd.read_csv(url_cdnow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFM aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2357 entries, 0 to 2356\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   customer_id  2357 non-null   int64  \n",
      " 1   frequency    2357 non-null   float64\n",
      " 2   recency      2357 non-null   float64\n",
      " 3   T            2357 non-null   float64\n",
      "dtypes: float64(3), int64(1)\n",
      "memory usage: 73.8 KB\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
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       "      <td>3.0</td>\n",
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       "   customer_id  frequency  recency     T\n",
       "0            1        3.0     49.0  78.0\n",
       "1            2        1.0      2.0  78.0\n",
       "2            3        0.0      0.0  78.0\n",
       "3            4        0.0      0.0  78.0\n",
       "4            5        0.0      0.0  78.0"
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     "execution_count": 12,
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   "source": [
    "rfm_data = clv.rfm_summary(\n",
    "    raw_data,\n",
    "    customer_id_col=\"id\",\n",
    "    datetime_col=\"date\",\n",
    "    datetime_format=\"%Y%m%d\",\n",
    "    time_unit=\"W\",\n",
    ")\n",
    "\n",
    "rfm_data.info()\n",
    "rfm_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ParetoNBDModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAP fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pareto/NBD\n",
       "            alpha ~ Weibull(2, 10)\n",
       "             beta ~ Weibull(2, 10)\n",
       "                r ~ Weibull(2, 1)\n",
       "                s ~ Weibull(2, 1)\n",
       "recency_frequency ~ ParetoNBD(r, alpha, s, beta, <constant>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnbd_map = clv.ParetoNBDModel(data=rfm_data)\n",
    "pnbd_map.build_model()  # required for prior predictive checks\n",
    "pnbd_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta, r, recency_frequency, s]\n"
     ]
    }
   ],
   "source": [
    "with pnbd_map.model:\n",
    "    prior_idata = pm.sample_prior_predictive(random_seed=45, samples=1)\n",
    "\n",
    "obs_freq = prior_idata.observed_data[\"recency_frequency\"].sel(obs_var=\"frequency\")\n",
    "ppc_freq = prior_idata.prior_predictive[\"recency_frequency\"].sel(obs_var=\"frequency\")[\n",
    "    0\n",
    "][0]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "Output()"
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     "text": [
      "Sampling: [recency_frequency]\n"
     ]
    },
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       "Output()"
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   "source": [
    "pnbd_map.fit()\n",
    "map_fit = pnbd_map.fit_summary()  # save for plotting later\n",
    "\n",
    "obs_freq = pnbd_map.idata.observed_data[\"recency_frequency\"].sel(obs_var=\"frequency\")\n",
    "ppc_freq = pnbd_map.distribution_new_customer_recency_frequency(\n",
    "    rfm_data,\n",
    "    random_seed=42,\n",
    ").sel(chain=0, draw=0, obs_var=\"frequency\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DEMZ fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "DEMetropolisZ: [alpha, beta, r, s]\n"
     ]
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       "Output()"
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     "text": [
      "Sampling 4 chains for 2_500 tune and 3_000 draw iterations (10_000 + 12_000 draws total) took 9 seconds.\n"
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       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 408kB\n",
       "Dimensions:  (chain: 4, draw: 3000)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 32B 0 1 2 3\n",
       "  * draw     (draw) int64 24kB 0 1 2 3 4 5 6 ... 2994 2995 2996 2997 2998 2999\n",
       "Data variables:\n",
       "    alpha    (chain, draw) float64 96kB 14.55 14.55 14.55 ... 15.5 15.5 15.5\n",
       "    beta     (chain, draw) float64 96kB 13.6 13.6 13.6 ... 16.08 16.08 16.08\n",
       "    r        (chain, draw) float64 96kB 0.578 0.578 0.578 ... 0.5896 0.5896\n",
       "    s        (chain, draw) float64 96kB 0.4404 0.4404 0.4404 ... 0.4612 0.4612\n",
       "Attributes:\n",
       "    created_at:                 2025-01-15T17:10:23.583479+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0\n",
       "    sampling_time:              8.844023704528809\n",
       "    tuning_steps:               2500</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-36048384-a6df-49a2-9c75-06c072db105f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36048384-a6df-49a2-9c75-06c072db105f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 3000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fa9f3d57-d027-4d1a-b78e-14debf654eeb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa9f3d57-d027-4d1a-b78e-14debf654eeb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-835c15e0-a05d-43ce-bc16-3c908db7ce94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-835c15e0-a05d-43ce-bc16-3c908db7ce94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c76131e4-3437-4268-a202-37da3a3c39cb' class='xr-var-data-in' type='checkbox'><label for='data-c76131e4-3437-4268-a202-37da3a3c39cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2996 2997 2998 2999</div><input id='attrs-b0a30f10-fe17-4834-bb52-da2f6cfda86d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0a30f10-fe17-4834-bb52-da2f6cfda86d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b642e461-9508-4616-a580-91d3c4c0a7a3' class='xr-var-data-in' type='checkbox'><label for='data-b642e461-9508-4616-a580-91d3c4c0a7a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2997, 2998, 2999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c1fd8dcc-4d0c-4bd7-95f5-9899570858b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c1fd8dcc-4d0c-4bd7-95f5-9899570858b5' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>14.55 14.55 14.55 ... 15.5 15.5</div><input id='attrs-010bacdf-528a-4eb0-ab0f-f2bfabccf725' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-010bacdf-528a-4eb0-ab0f-f2bfabccf725' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd18291c-2659-4271-9b95-08b5865e4e72' class='xr-var-data-in' type='checkbox'><label for='data-dd18291c-2659-4271-9b95-08b5865e4e72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[14.55451562, 14.55451562, 14.55451562, ..., 15.41604216,\n",
       "        15.41604216, 15.41604216],\n",
       "       [14.70110504, 14.70110504, 14.24184806, ..., 15.73584838,\n",
       "        16.01618443, 16.01618443],\n",
       "       [16.84317701, 16.84317701, 16.84317701, ..., 15.92597997,\n",
       "        15.92597997, 15.92597997],\n",
       "       [15.80148983, 15.80148983, 15.80148983, ..., 15.50056059,\n",
       "        15.50056059, 15.50056059]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.6 13.6 13.6 ... 16.08 16.08</div><input id='attrs-42f61263-550f-4074-bbd7-e0d1344972de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42f61263-550f-4074-bbd7-e0d1344972de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b920786-7b2f-44af-9bfe-4371499e988b' class='xr-var-data-in' type='checkbox'><label for='data-2b920786-7b2f-44af-9bfe-4371499e988b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[13.59541909, 13.59541909, 13.59541909, ..., 17.34596673,\n",
       "        17.34596673, 17.34596673],\n",
       "       [16.35281178, 16.35281178, 15.15761774, ..., 19.81520715,\n",
       "         9.40804265,  9.40804265],\n",
       "       [10.73037977, 10.73037977, 10.73037977, ..., 12.67859033,\n",
       "        12.67859033, 12.67859033],\n",
       "       [13.29498493, 13.29498493, 13.29498493, ..., 16.07591939,\n",
       "        16.07591939, 16.07591939]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.578 0.578 0.578 ... 0.5896 0.5896</div><input id='attrs-96ad5f37-4716-4352-97c6-145b8c948b71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96ad5f37-4716-4352-97c6-145b8c948b71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f36ba490-da08-4ccc-9d25-18e246509c74' class='xr-var-data-in' type='checkbox'><label for='data-f36ba490-da08-4ccc-9d25-18e246509c74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.57795849, 0.57795849, 0.57795849, ..., 0.63540714, 0.63540714,\n",
       "        0.63540714],\n",
       "       [0.58495046, 0.58495046, 0.5457837 , ..., 0.60981192, 0.62004183,\n",
       "        0.62004183],\n",
       "       [0.67970569, 0.67970569, 0.67970569, ..., 0.6169591 , 0.6169591 ,\n",
       "        0.6169591 ],\n",
       "       [0.6156909 , 0.6156909 , 0.6156909 , ..., 0.58955845, 0.58955845,\n",
       "        0.58955845]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4404 0.4404 ... 0.4612 0.4612</div><input id='attrs-5dd44979-88fa-4375-858f-41cb88e27079' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5dd44979-88fa-4375-858f-41cb88e27079' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60fec65e-fb1b-4d21-90d5-5652bc13b8df' class='xr-var-data-in' type='checkbox'><label for='data-60fec65e-fb1b-4d21-90d5-5652bc13b8df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44039204, 0.44039204, 0.44039204, ..., 0.52396709, 0.52396709,\n",
       "        0.52396709],\n",
       "       [0.48572963, 0.48572963, 0.42453578, ..., 0.54533813, 0.38977985,\n",
       "        0.38977985],\n",
       "       [0.42805226, 0.42805226, 0.42805226, ..., 0.43384109, 0.43384109,\n",
       "        0.43384109],\n",
       "       [0.41547868, 0.41547868, 0.41547868, ..., 0.46122496, 0.46122496,\n",
       "        0.46122496]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-249fd893-2f87-4c11-af41-b0fe7e317420' class='xr-section-summary-in' type='checkbox'  ><label for='section-249fd893-2f87-4c11-af41-b0fe7e317420' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6311b22a-fe0c-4085-96fa-bc10ec364f1d' class='xr-index-data-in' type='checkbox'/><label for='index-6311b22a-fe0c-4085-96fa-bc10ec364f1d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0e017b7f-169a-4d86-a617-311bcbff07e5' class='xr-index-data-in' type='checkbox'/><label for='index-0e017b7f-169a-4d86-a617-311bcbff07e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=3000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00b48b7a-501e-450e-9086-4508f7ecb71e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00b48b7a-501e-450e-9086-4508f7ecb71e' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-01-15T17:10:23.583479+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd><dt><span>sampling_time :</span></dt><dd>8.844023704528809</dd><dt><span>tuning_steps :</span></dt><dd>2500</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_log_likelihoode9f74d70-b271-4363-863d-c955073f1b7a\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_log_likelihoode9f74d70-b271-4363-863d-c955073f1b7a\" class = \"xr-section-summary\">log_likelihood</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 226MB\n",
       "Dimensions:            (chain: 4, draw: 3000, customer_id: 2357)\n",
       "Coordinates:\n",
       "  * chain              (chain) int64 32B 0 1 2 3\n",
       "  * draw               (draw) int64 24kB 0 1 2 3 4 ... 2995 2996 2997 2998 2999\n",
       "  * customer_id        (customer_id) int64 19kB 1 2 3 4 ... 2354 2355 2356 2357\n",
       "Data variables:\n",
       "    recency_frequency  (chain, draw, customer_id) float64 226MB -14.31 ... -0...\n",
       "Attributes:\n",
       "    created_at:                 2025-01-15T17:10:28.153932+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5e4f9d4b-24a0-4c9b-a359-7a266fe13b53' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5e4f9d4b-24a0-4c9b-a359-7a266fe13b53' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 3000</li><li><span class='xr-has-index'>customer_id</span>: 2357</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7ddb2496-5093-4a00-92ae-120be6114822' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ddb2496-5093-4a00-92ae-120be6114822' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-8e0d4350-eb6b-4fcf-a843-c1006588ed8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e0d4350-eb6b-4fcf-a843-c1006588ed8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f328ea4-c0a3-4fb4-848b-711f6581e66c' class='xr-var-data-in' type='checkbox'><label for='data-2f328ea4-c0a3-4fb4-848b-711f6581e66c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2996 2997 2998 2999</div><input id='attrs-acb23472-01a9-41fe-b68f-125e78172131' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acb23472-01a9-41fe-b68f-125e78172131' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca8ed308-9fa3-4707-925e-4abb1caecf28' class='xr-var-data-in' type='checkbox'><label for='data-ca8ed308-9fa3-4707-925e-4abb1caecf28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2997, 2998, 2999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>customer_id</span></div><div class='xr-var-dims'>(customer_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2354 2355 2356 2357</div><input id='attrs-424f4410-cca8-42cc-b645-cce4f6e1c44c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-424f4410-cca8-42cc-b645-cce4f6e1c44c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51c54051-c2af-4303-a210-6d7e5c4b425e' class='xr-var-data-in' type='checkbox'><label for='data-51c54051-c2af-4303-a210-6d7e5c4b425e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2355, 2356, 2357])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f4e55d9-1206-4014-9a8b-e5e41728021a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6f4e55d9-1206-4014-9a8b-e5e41728021a' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>recency_frequency</span></div><div class='xr-var-dims'>(chain, draw, customer_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-14.31 -4.881 ... -22.7 -0.6275</div><input id='attrs-53ba72fb-54cd-4407-b972-fd7886468858' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53ba72fb-54cd-4407-b972-fd7886468858' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-776696c2-2397-4946-aecf-ed4d8fcad4e9' class='xr-var-data-in' type='checkbox'><label for='data-776696c2-2397-4946-aecf-ed4d8fcad4e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-14.30676082,  -4.8811313 ,  -0.64469637, ...,  -6.52183588,\n",
       "         -22.70410346,  -0.62063786],\n",
       "        [-14.30676082,  -4.8811313 ,  -0.64469637, ...,  -6.52183588,\n",
       "         -22.70410346,  -0.62063786],\n",
       "        [-14.30676082,  -4.8811313 ,  -0.64469637, ...,  -6.52183588,\n",
       "         -22.70410346,  -0.62063786],\n",
       "        ...,\n",
       "        [-14.26847389,  -4.82881749,  -0.67712998, ...,  -6.52643541,\n",
       "         -22.68579291,  -0.65314439],\n",
       "        [-14.26847389,  -4.82881749,  -0.67712998, ...,  -6.52643541,\n",
       "         -22.68579291,  -0.65314439],\n",
       "        [-14.26847389,  -4.82881749,  -0.67712998, ...,  -6.52643541,\n",
       "         -22.68579291,  -0.65314439]],\n",
       "\n",
       "       [[-14.29210909,  -4.88392248,  -0.65431983, ...,  -6.51501264,\n",
       "         -22.70351168,  -0.63018104],\n",
       "        [-14.29210909,  -4.88392248,  -0.65431983, ...,  -6.51501264,\n",
       "         -22.70351168,  -0.63018104],\n",
       "        [-14.28701737,  -4.96348839,  -0.64618366, ...,  -6.47512967,\n",
       "         -22.68329627,  -0.62058608],\n",
       "...\n",
       "        [-14.28738767,  -4.85646402,  -0.64549993, ...,  -6.50863806,\n",
       "         -22.70978359,  -0.62112619],\n",
       "        [-14.28738767,  -4.85646402,  -0.64549993, ...,  -6.50863806,\n",
       "         -22.70978359,  -0.62112619],\n",
       "        [-14.28738767,  -4.85646402,  -0.64549993, ...,  -6.50863806,\n",
       "         -22.70978359,  -0.62112619]],\n",
       "\n",
       "       [[-14.25086817,  -4.89345854,  -0.66711106, ...,  -6.47018123,\n",
       "         -22.65798619,  -0.64097559],\n",
       "        [-14.25086817,  -4.89345854,  -0.66711106, ...,  -6.47018123,\n",
       "         -22.65798619,  -0.64097559],\n",
       "        [-14.25086817,  -4.89345854,  -0.66711106, ...,  -6.47018123,\n",
       "         -22.65798619,  -0.64097559],\n",
       "        ...,\n",
       "        [-14.26873791,  -4.91706747,  -0.65286167, ...,  -6.475441  ,\n",
       "         -22.69755527,  -0.62751066],\n",
       "        [-14.26873791,  -4.91706747,  -0.65286167, ...,  -6.475441  ,\n",
       "         -22.69755527,  -0.62751066],\n",
       "        [-14.26873791,  -4.91706747,  -0.65286167, ...,  -6.475441  ,\n",
       "         -22.69755527,  -0.62751066]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8c4d60c6-f439-40df-8f31-3f6d3e061ec3' class='xr-section-summary-in' type='checkbox'  ><label for='section-8c4d60c6-f439-40df-8f31-3f6d3e061ec3' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-34fa062f-9c1a-48ed-865c-851e9cb2c5b6' class='xr-index-data-in' type='checkbox'/><label for='index-34fa062f-9c1a-48ed-865c-851e9cb2c5b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cd637586-f20c-4965-ae52-50aec4dc43e7' class='xr-index-data-in' type='checkbox'/><label for='index-cd637586-f20c-4965-ae52-50aec4dc43e7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=3000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>customer_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0c7a1c55-2f1d-4e28-957e-3a85bd5eb3e7' class='xr-index-data-in' type='checkbox'/><label for='index-0c7a1c55-2f1d-4e28-957e-3a85bd5eb3e7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "       ...\n",
       "       2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;customer_id&#x27;, length=2357))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-654fee99-86aa-414d-96ea-ae87f27463bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-654fee99-86aa-414d-96ea-ae87f27463bb' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-01-15T17:10:28.153932+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats76e9bc73-b1c2-4d03-a466-d91e6dbd57f7\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats76e9bc73-b1c2-4d03-a466-d91e6dbd57f7\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 324kB\n",
       "Dimensions:   (chain: 4, draw: 3000)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 32B 0 1 2 3\n",
       "  * draw      (draw) int64 24kB 0 1 2 3 4 5 6 ... 2994 2995 2996 2997 2998 2999\n",
       "Data variables:\n",
       "    scaling   (chain, draw) float64 96kB 0.0003452 0.0003452 ... 0.0003138\n",
       "    lambda    (chain, draw) float64 96kB 0.8415 0.8415 0.8415 ... 0.8415 0.8415\n",
       "    accepted  (chain, draw) bool 12kB False False False ... True False False\n",
       "    accept    (chain, draw) float64 96kB 0.06942 0.06737 ... 0.06106 0.05633\n",
       "Attributes:\n",
       "    created_at:                 2025-01-15T17:10:23.586539+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0\n",
       "    sampling_time:              8.844023704528809\n",
       "    tuning_steps:               2500</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b0b7e27b-e471-4301-b22e-2eb9b830e764' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b0b7e27b-e471-4301-b22e-2eb9b830e764' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 3000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c8d2ab11-5556-4781-b377-1d920cc76ab9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8d2ab11-5556-4781-b377-1d920cc76ab9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-35c0606e-5f0e-49e6-8ee5-e1256eabb7aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35c0606e-5f0e-49e6-8ee5-e1256eabb7aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6305221c-c782-454e-bd9e-610fc79cde4a' class='xr-var-data-in' type='checkbox'><label for='data-6305221c-c782-454e-bd9e-610fc79cde4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2996 2997 2998 2999</div><input id='attrs-4215fb7f-faa3-4466-8848-9aa05d526205' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4215fb7f-faa3-4466-8848-9aa05d526205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-197af2d5-ee48-4b85-bbf5-5dc616b0e237' class='xr-var-data-in' type='checkbox'><label for='data-197af2d5-ee48-4b85-bbf5-5dc616b0e237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2997, 2998, 2999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2698ff40-1bcb-4019-b9e8-3dc2ea66052f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2698ff40-1bcb-4019-b9e8-3dc2ea66052f' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>scaling</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0003452 0.0003452 ... 0.0003138</div><input id='attrs-b0c2be2b-cfa4-4e0d-ac49-4172c5b700cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0c2be2b-cfa4-4e0d-ac49-4172c5b700cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e84f201-f502-4072-9419-37172b354f13' class='xr-var-data-in' type='checkbox'><label for='data-9e84f201-f502-4072-9419-37172b354f13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00034519, 0.00034519, 0.00034519, ..., 0.00034519, 0.00034519,\n",
       "        0.00034519],\n",
       "       [0.00013509, 0.00013509, 0.00013509, ..., 0.00013509, 0.00013509,\n",
       "        0.00013509],\n",
       "       [0.00013509, 0.00013509, 0.00013509, ..., 0.00013509, 0.00013509,\n",
       "        0.00013509],\n",
       "       [0.00031381, 0.00031381, 0.00031381, ..., 0.00031381, 0.00031381,\n",
       "        0.00031381]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambda</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8415 0.8415 ... 0.8415 0.8415</div><input id='attrs-e2386d66-85d1-4324-b800-e7d61a752a0c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2386d66-85d1-4324-b800-e7d61a752a0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92717a3e-d7c3-4db8-b685-5ce1c316f18c' class='xr-var-data-in' type='checkbox'><label for='data-92717a3e-d7c3-4db8-b685-5ce1c316f18c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.84145707, 0.84145707, 0.84145707, ..., 0.84145707, 0.84145707,\n",
       "        0.84145707],\n",
       "       [0.84145707, 0.84145707, 0.84145707, ..., 0.84145707, 0.84145707,\n",
       "        0.84145707],\n",
       "       [0.84145707, 0.84145707, 0.84145707, ..., 0.84145707, 0.84145707,\n",
       "        0.84145707],\n",
       "       [0.84145707, 0.84145707, 0.84145707, ..., 0.84145707, 0.84145707,\n",
       "        0.84145707]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accepted</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-be68aaa2-49d6-4608-92af-de935114ecb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be68aaa2-49d6-4608-92af-de935114ecb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-252bd596-1f15-4b38-8a29-c3a882a83a02' class='xr-var-data-in' type='checkbox'><label for='data-252bd596-1f15-4b38-8a29-c3a882a83a02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ...,  True, False, False],\n",
       "       [False, False,  True, ..., False,  True, False],\n",
       "       [ True, False, False, ..., False, False, False],\n",
       "       [False, False, False, ...,  True, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06942 0.06737 ... 0.06106 0.05633</div><input id='attrs-a16accd9-d4b7-4add-8f4c-59f05824dcb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a16accd9-d4b7-4add-8f4c-59f05824dcb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f349cfd-34d8-41d7-b7a6-34c0b17ff402' class='xr-var-data-in' type='checkbox'><label for='data-1f349cfd-34d8-41d7-b7a6-34c0b17ff402' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6.94240988e-02, 6.73707292e-02, 4.39720413e-01, ...,\n",
       "        2.24015305e+00, 1.78664007e-03, 3.60890012e-01],\n",
       "       [1.02053169e-03, 1.87827917e-02, 3.20767037e-01, ...,\n",
       "        9.26937988e-02, 1.06793856e+00, 7.70146018e-02],\n",
       "       [7.46513073e-01, 8.61873172e-03, 1.81044662e-02, ...,\n",
       "        1.65268918e-01, 1.47516147e-01, 2.41023489e-02],\n",
       "       [6.75100632e-02, 2.64666512e-03, 4.83285898e-01, ...,\n",
       "        1.04195093e+00, 6.10612656e-02, 5.63312648e-02]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-338e52d1-9064-4453-92fc-47a9b7342354' class='xr-section-summary-in' type='checkbox'  ><label for='section-338e52d1-9064-4453-92fc-47a9b7342354' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-57091927-14f8-4c09-a5cb-95e219cc4648' class='xr-index-data-in' type='checkbox'/><label for='index-57091927-14f8-4c09-a5cb-95e219cc4648' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c66beb79-f483-4cfe-ba11-3168ad34830a' class='xr-index-data-in' type='checkbox'/><label for='index-c66beb79-f483-4cfe-ba11-3168ad34830a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=3000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e3f7e6c-73ad-4def-9d6d-9519d66cb090' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e3f7e6c-73ad-4def-9d6d-9519d66cb090' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-01-15T17:10:23.586539+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd><dt><span>sampling_time :</span></dt><dd>8.844023704528809</dd><dt><span>tuning_steps :</span></dt><dd>2500</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_data4a056664-5269-432f-9b5c-15e9b40fb747\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data4a056664-5269-432f-9b5c-15e9b40fb747\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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       "\n",
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
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       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
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       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 57kB\n",
       "Dimensions:            (customer_id: 2357, obs_var: 2)\n",
       "Coordinates:\n",
       "  * customer_id        (customer_id) int64 19kB 1 2 3 4 ... 2354 2355 2356 2357\n",
       "  * obs_var            (obs_var) &lt;U9 72B &#x27;recency&#x27; &#x27;frequency&#x27;\n",
       "Data variables:\n",
       "    recency_frequency  (customer_id, obs_var) float64 38kB 49.0 3.0 ... 0.0 0.0\n",
       "Attributes:\n",
       "    created_at:                 2025-01-15T17:10:23.589128+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e15b4894-f8ab-477b-bf04-f51e5b9d924e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e15b4894-f8ab-477b-bf04-f51e5b9d924e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>customer_id</span>: 2357</li><li><span class='xr-has-index'>obs_var</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9ff9451f-e7e0-440d-b804-0194adc95370' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ff9451f-e7e0-440d-b804-0194adc95370' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>customer_id</span></div><div class='xr-var-dims'>(customer_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2354 2355 2356 2357</div><input id='attrs-d32d9a37-28e5-4861-a882-2bc4fcfd1dee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d32d9a37-28e5-4861-a882-2bc4fcfd1dee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6381ba4e-b18b-4e51-8303-0a7b9e41e4de' class='xr-var-data-in' type='checkbox'><label for='data-6381ba4e-b18b-4e51-8303-0a7b9e41e4de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2355, 2356, 2357])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs_var</span></div><div class='xr-var-dims'>(obs_var)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;recency&#x27; &#x27;frequency&#x27;</div><input id='attrs-b30acf1d-27f8-4bcc-90ac-159ccb4c0f09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b30acf1d-27f8-4bcc-90ac-159ccb4c0f09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b788479-b1ca-4e2e-97b7-ec8b362d855a' class='xr-var-data-in' type='checkbox'><label for='data-4b788479-b1ca-4e2e-97b7-ec8b362d855a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;recency&#x27;, &#x27;frequency&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30d849d7-a9c4-4239-8b0e-48c506354d6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-30d849d7-a9c4-4239-8b0e-48c506354d6f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>recency_frequency</span></div><div class='xr-var-dims'>(customer_id, obs_var)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.0 3.0 2.0 1.0 ... 6.0 0.0 0.0</div><input id='attrs-73b1c77d-3dc0-458b-b013-6ac289f9bda0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73b1c77d-3dc0-458b-b013-6ac289f9bda0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac3fec27-e4bc-4e28-983f-efee330037f3' class='xr-var-data-in' type='checkbox'><label for='data-ac3fec27-e4bc-4e28-983f-efee330037f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[49.,  3.],\n",
       "       [ 2.,  1.],\n",
       "       [ 0.,  0.],\n",
       "       ...,\n",
       "       [44.,  1.],\n",
       "       [62.,  6.],\n",
       "       [ 0.,  0.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8880ae79-ea45-4067-ac4d-af84203c626f' class='xr-section-summary-in' type='checkbox'  ><label for='section-8880ae79-ea45-4067-ac4d-af84203c626f' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>customer_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cc0dbda5-2c54-446f-b097-a884f2e046bc' class='xr-index-data-in' type='checkbox'/><label for='index-cc0dbda5-2c54-446f-b097-a884f2e046bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "       ...\n",
       "       2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;customer_id&#x27;, length=2357))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs_var</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-42ac2a4a-c0ce-4dbd-8d1b-9168af404363' class='xr-index-data-in' type='checkbox'/><label for='index-42ac2a4a-c0ce-4dbd-8d1b-9168af404363' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;recency&#x27;, &#x27;frequency&#x27;], dtype=&#x27;object&#x27;, name=&#x27;obs_var&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2df7509-c8c0-454d-b589-49ec9681dfcb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2df7509-c8c0-454d-b589-49ec9681dfcb' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-01-15T17:10:23.589128+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd></dl></div></li></ul></div></div><br></div>\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       ".xr-header {\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
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       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 94kB\n",
       "Dimensions:      (index: 2357)\n",
       "Coordinates:\n",
       "  * index        (index) int64 19kB 0 1 2 3 4 5 ... 2352 2353 2354 2355 2356\n",
       "Data variables:\n",
       "    customer_id  (index) int64 19kB 1 2 3 4 5 6 ... 2353 2354 2355 2356 2357\n",
       "    frequency    (index) float64 19kB 3.0 1.0 0.0 0.0 0.0 ... 5.0 1.0 6.0 0.0\n",
       "    recency      (index) float64 19kB 49.0 2.0 0.0 0.0 ... 24.0 44.0 62.0 0.0\n",
       "    T            (index) float64 19kB 78.0 78.0 78.0 78.0 ... 66.0 66.0 66.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-701197df-764d-4582-8f69-ec9611ccd6a4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-701197df-764d-4582-8f69-ec9611ccd6a4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>index</span>: 2357</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3985907b-b5c5-4781-a4b6-141ac376cd5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3985907b-b5c5-4781-a4b6-141ac376cd5f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2353 2354 2355 2356</div><input id='attrs-5ded61c6-ce05-4c17-b14d-20f26384813c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ded61c6-ce05-4c17-b14d-20f26384813c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-577214fc-9575-42cd-8739-2fd0c54c698a' class='xr-var-data-in' type='checkbox'><label for='data-577214fc-9575-42cd-8739-2fd0c54c698a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2354, 2355, 2356])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a31c6aa-9dce-4bd7-a49e-931eb3234c0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a31c6aa-9dce-4bd7-a49e-931eb3234c0a' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>customer_id</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2354 2355 2356 2357</div><input id='attrs-687efaec-c07e-490e-9525-cfcf70ae6825' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-687efaec-c07e-490e-9525-cfcf70ae6825' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f81b77f-5b69-4e33-95e3-a3fd6c23e742' class='xr-var-data-in' type='checkbox'><label for='data-3f81b77f-5b69-4e33-95e3-a3fd6c23e742' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2355, 2356, 2357])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 1.0 0.0 0.0 ... 5.0 1.0 6.0 0.0</div><input id='attrs-3a9a8164-1871-4ab3-82d0-f77c7b41e8af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a9a8164-1871-4ab3-82d0-f77c7b41e8af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-211c4521-fa42-407f-a645-512390cf8986' class='xr-var-data-in' type='checkbox'><label for='data-211c4521-fa42-407f-a645-512390cf8986' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3., 1., 0., ..., 1., 6., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.0 2.0 0.0 0.0 ... 44.0 62.0 0.0</div><input id='attrs-3a579844-1521-42f7-a4f7-8cafed9d4e40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a579844-1521-42f7-a4f7-8cafed9d4e40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5db94fec-5428-45a9-84c3-836ac6a2bc63' class='xr-var-data-in' type='checkbox'><label for='data-5db94fec-5428-45a9-84c3-836ac6a2bc63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([49.,  2.,  0., ..., 44., 62.,  0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>78.0 78.0 78.0 ... 66.0 66.0 66.0</div><input id='attrs-be6b5e66-f959-4997-b4d3-12bfc3ab73f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be6b5e66-f959-4997-b4d3-12bfc3ab73f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b5f79cb-d539-4221-aa28-c4d5635abffc' class='xr-var-data-in' type='checkbox'><label for='data-8b5f79cb-d539-4221-aa28-c4d5635abffc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([78., 78., 78., ..., 66., 66., 66.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d98378d3-0410-4dac-a4ab-3c5bf13d6d76' class='xr-section-summary-in' type='checkbox'  ><label for='section-d98378d3-0410-4dac-a4ab-3c5bf13d6d76' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>index</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cee6b8be-5677-4003-82c8-66318710e153' class='xr-index-data-in' type='checkbox'/><label for='index-cee6b8be-5677-4003-82c8-66318710e153' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=2357, step=1, name=&#x27;index&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a521cbcc-b21a-4e51-bb17-a6e666cf7012' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a521cbcc-b21a-4e51-bb17-a6e666cf7012' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
       "html[theme=dark],\n",
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       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-sections.group-sections {\n",
       "  grid-template-columns: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
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      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> observed_data\n",
       "\t> fit_data"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnbd_full = clv.ParetoNBDModel(data=rfm_data)\n",
    "pnbd_full.fit(\n",
    "    fit_method=\"demz\", draws=3000, tune=2500, idata_kwargs={\"log_likelihood\": True}\n",
    ")"
   ]
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   "cell_type": "code",
   "execution_count": 18,
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       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
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       "      <th>alpha</th>\n",
       "      <td>15.614</td>\n",
       "      <td>1.050</td>\n",
       "      <td>13.668</td>\n",
       "      <td>17.537</td>\n",
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       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>12.750</td>\n",
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      "text/plain": [
       "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha  15.614  1.050  13.668   17.537      0.036    0.026     862.0     996.0   \n",
       "beta   12.750  3.691   6.362   19.946      0.135    0.096     739.0    1323.0   \n",
       "r       0.619  0.046   0.542    0.712      0.002    0.001     855.0     898.0   \n",
       "s       0.431  0.060   0.320    0.544      0.002    0.001     824.0    1251.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    1.0  \n",
       "beta     1.0  \n",
       "r        1.0  \n",
       "s        1.0  "
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   "source": [
    "pnbd_full.fit_summary()"
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   "metadata": {},
   "source": [
    "### ADVI fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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       "Output()"
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      "Finished [100%]: Average Loss = 16,486\n"
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       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20kB\n",
       "Dimensions:  (chain: 1, draw: 500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 8B 0\n",
       "  * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "Data variables:\n",
       "    alpha    (chain, draw) float64 4kB 15.74 15.65 14.86 ... 16.01 15.87 15.23\n",
       "    beta     (chain, draw) float64 4kB 12.93 12.05 12.89 ... 14.93 11.61 11.7\n",
       "    r        (chain, draw) float64 4kB 0.6118 0.5966 0.5927 ... 0.6028 0.6444\n",
       "    s        (chain, draw) float64 4kB 0.4536 0.4336 0.4428 ... 0.4636 0.4284\n",
       "Attributes:\n",
       "    created_at:                 2025-01-15T18:22:23.502516+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a1413846-6b00-438f-b308-522f9b368095' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a1413846-6b00-438f-b308-522f9b368095' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-274659dd-f972-439b-bfca-3fe8902c25b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-274659dd-f972-439b-bfca-3fe8902c25b5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ae22ee37-dc43-441a-9aa7-8732b59ccf70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae22ee37-dc43-441a-9aa7-8732b59ccf70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2c1ef75-d4b5-4b53-b7ef-564898817951' class='xr-var-data-in' type='checkbox'><label for='data-e2c1ef75-d4b5-4b53-b7ef-564898817951' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-0012f71f-ec76-41ec-b00a-47f15d76abec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0012f71f-ec76-41ec-b00a-47f15d76abec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c0c2703-1b70-4aa2-baae-4ee3dc6951d6' class='xr-var-data-in' type='checkbox'><label for='data-8c0c2703-1b70-4aa2-baae-4ee3dc6951d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48ecdab0-086b-4ed2-89be-79f1490af92c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48ecdab0-086b-4ed2-89be-79f1490af92c' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.74 15.65 14.86 ... 15.87 15.23</div><input id='attrs-bbd69c8f-f463-4ced-bf94-0e7834712e0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bbd69c8f-f463-4ced-bf94-0e7834712e0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f00fad3-e4f1-4249-aa9a-6f7e07d361d1' class='xr-var-data-in' type='checkbox'><label for='data-5f00fad3-e4f1-4249-aa9a-6f7e07d361d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[15.7367195 , 15.65055955, 14.86158487, 16.68547241, 15.58549388,\n",
       "        15.69024317, 15.7180027 , 15.4809491 , 15.92093634, 16.69412463,\n",
       "        16.03626216, 15.58730181, 16.45592133, 16.08468023, 15.39111114,\n",
       "        15.99666778, 15.46622552, 15.60409054, 15.54954566, 15.60736394,\n",
       "        14.88510044, 15.95385506, 15.64284305, 14.926699  , 14.67146242,\n",
       "        15.52952109, 16.05029449, 16.00786548, 15.67788174, 15.33407737,\n",
       "        15.32061667, 15.10771972, 15.29393198, 14.90074869, 15.87781429,\n",
       "        14.99790971, 15.64506619, 15.08001955, 14.94125219, 15.21157164,\n",
       "        15.47870378, 15.81169221, 16.25086231, 15.7750182 , 15.65051979,\n",
       "        14.88623742, 14.89583716, 15.34663153, 15.53880869, 15.13230232,\n",
       "        15.27568435, 15.35253918, 16.4246506 , 14.51761145, 15.38963059,\n",
       "        16.26017807, 15.22034272, 15.63204613, 16.69068568, 15.48721818,\n",
       "        15.72085971, 15.96066345, 15.87433939, 16.72906264, 15.29926511,\n",
       "        14.57724087, 15.84520407, 14.97851562, 15.43067024, 14.14222232,\n",
       "        16.26083803, 15.70194847, 15.95911564, 15.93177512, 16.25517864,\n",
       "        15.37841735, 15.84651753, 14.56142804, 16.42368342, 16.30394827,\n",
       "        15.43184798, 15.55376857, 16.1618587 , 16.07548749, 15.08941792,\n",
       "        15.38766983, 15.52656481, 14.88386755, 14.65952573, 16.22116991,\n",
       "        16.10672043, 16.24036876, 16.2382048 , 15.78211723, 15.25383674,\n",
       "        15.28189892, 14.92630784, 14.98445112, 14.70204235, 15.50199702,\n",
       "...\n",
       "        14.51389553, 15.11281618, 15.69320895, 15.39438312, 15.3725994 ,\n",
       "        15.27876712, 15.51263473, 15.58364557, 15.68664884, 15.38231782,\n",
       "        15.31416475, 14.822525  , 16.06073432, 16.64787556, 15.28882645,\n",
       "        15.87018448, 15.09048931, 15.33362807, 13.91642106, 15.44929324,\n",
       "        15.73158478, 15.87694899, 16.45812664, 16.20421409, 16.47846295,\n",
       "        14.54917403, 14.8476345 , 16.34056377, 15.21236574, 14.87343589,\n",
       "        15.44888559, 15.21490278, 15.4291098 , 15.81969286, 15.45455701,\n",
       "        14.4304194 , 16.38998671, 15.95701114, 15.77487479, 15.0601574 ,\n",
       "        14.85405858, 14.99879375, 15.36660642, 14.37671145, 14.83751259,\n",
       "        16.50638648, 15.70479022, 16.41504285, 15.54740202, 15.1689825 ,\n",
       "        16.54166985, 16.2812993 , 14.85347261, 15.39534891, 15.29131261,\n",
       "        14.97589078, 14.62873947, 14.98097488, 16.13457091, 14.92129778,\n",
       "        15.84668778, 15.43612882, 14.80715707, 15.26506001, 15.19454365,\n",
       "        15.5395255 , 16.53625966, 16.02624971, 16.78353141, 16.3110358 ,\n",
       "        16.5920845 , 14.9436361 , 16.3888876 , 16.48454675, 15.80412096,\n",
       "        15.32851223, 15.11137942, 15.94104321, 15.29172967, 14.41252068,\n",
       "        14.45050674, 16.79094449, 15.22414392, 14.93888976, 15.03443704,\n",
       "        15.88336441, 15.99934791, 15.34288895, 16.11846383, 15.04710398,\n",
       "        14.55428198, 16.05833204, 15.3248353 , 16.56819117, 16.9640813 ,\n",
       "        15.76093579, 15.88794768, 16.00724858, 15.87388446, 15.23298712]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>12.93 12.05 12.89 ... 11.61 11.7</div><input id='attrs-5ede3753-b2fd-4de8-bcc3-6b2816e4ac71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ede3753-b2fd-4de8-bcc3-6b2816e4ac71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4dce810-c3e7-4b37-9662-2f6d23d6f156' class='xr-var-data-in' type='checkbox'><label for='data-a4dce810-c3e7-4b37-9662-2f6d23d6f156' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[12.9313082 , 12.04647786, 12.88739769, 12.73734112, 12.2774913 ,\n",
       "         9.97318783, 12.8326986 , 13.08561348, 12.1802583 , 12.0025272 ,\n",
       "        13.42808843, 12.08295488, 13.46259753, 13.04622822, 13.30566555,\n",
       "        14.10768868, 13.51015636, 12.10754699, 12.60545152, 11.91304283,\n",
       "        14.81192556, 11.95884077, 15.91013334, 11.90069222, 11.24659883,\n",
       "        13.35196781, 13.20691197, 13.04008385, 14.28968208, 12.27304291,\n",
       "        11.90131065, 13.6887336 , 11.52551525, 11.9903933 , 12.50566375,\n",
       "        13.56500685, 14.53995139, 11.55981676, 12.47056111, 13.03747597,\n",
       "        13.83488419, 12.68730568, 14.46392821, 10.80810357, 14.15590437,\n",
       "        11.57125784, 12.78551571, 12.06427056, 11.59661568, 13.79946228,\n",
       "        13.88618554, 11.53712798, 12.06529965, 14.8959815 , 11.91583731,\n",
       "        12.76606442, 12.33664601, 12.72791845, 10.83674468, 11.94931224,\n",
       "        13.27060869, 12.73777202, 12.14529083, 11.54101497, 14.54920611,\n",
       "        11.93094694, 11.83664328, 13.64567975, 11.85092447, 13.20030558,\n",
       "        13.38357253, 13.28123569, 12.76352707, 13.32129051, 11.23830839,\n",
       "        12.83616252, 12.77162145, 11.77769495, 13.81026287, 11.22019028,\n",
       "        11.81404144, 13.155239  , 13.33569298, 16.20089461, 13.49582913,\n",
       "        12.05227727, 13.01519889, 12.80262574,  9.75347314, 14.21595974,\n",
       "        13.43289601, 13.60119978, 14.02548687, 13.28846133, 12.01114365,\n",
       "        10.83178717, 12.27943641, 11.85541989, 12.23654732, 12.89551976,\n",
       "...\n",
       "        13.11979789, 13.27690101, 11.260477  , 12.65648598, 13.25346396,\n",
       "        10.96444287, 12.1973514 , 13.36305279, 13.40735341, 12.80159449,\n",
       "        10.43005228, 12.65674117, 10.73809249, 12.32021774, 13.76482143,\n",
       "        11.62122299, 14.4406761 , 11.18661314, 11.40063195, 13.80096525,\n",
       "        10.72768358, 13.02093813, 12.15526828, 12.82293504, 12.04141541,\n",
       "        11.26415585, 12.14655549, 13.04882568, 11.63395494, 12.68168112,\n",
       "        12.63989342, 10.85069875, 11.53128942, 12.1461875 , 11.67855285,\n",
       "        10.62616514, 12.20177035, 10.24514157, 10.72820439, 11.69542158,\n",
       "        12.20313853, 10.91838357, 13.02774785, 14.04711102, 10.39054203,\n",
       "        13.07249187, 13.63331147, 12.53802172, 11.46478557, 13.1285072 ,\n",
       "        11.66980032, 11.45442145, 12.84796046, 15.64361274, 12.27430938,\n",
       "        13.01502713, 12.25789859, 12.9352568 , 13.05474265, 12.1958682 ,\n",
       "        11.35226219, 11.22891069, 11.94297063, 12.48481293, 13.0237427 ,\n",
       "        13.67646426,  9.6118426 , 11.72011091, 13.82876899, 15.20097646,\n",
       "        12.30549864, 11.37562021, 13.24397446, 12.75931299, 14.04421154,\n",
       "        11.77234559, 11.2475118 , 11.18972109, 10.63980326, 12.58681972,\n",
       "        12.93664867, 11.56817881, 12.62484246, 12.37793181, 14.08427804,\n",
       "        11.43173261, 14.80840449, 11.8071163 , 10.83400963, 12.56448921,\n",
       "        11.01736983, 11.38639038, 11.99273945, 11.73697351, 11.60279781,\n",
       "        11.47754329, 12.03186243, 14.93274463, 11.61201074, 11.70427281]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6118 0.5966 ... 0.6028 0.6444</div><input id='attrs-bbc9d407-8fad-442d-bfe5-f1931b24284e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bbc9d407-8fad-442d-bfe5-f1931b24284e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25b769a6-d00b-4bef-b4ab-15bc33d17501' class='xr-var-data-in' type='checkbox'><label for='data-25b769a6-d00b-4bef-b4ab-15bc33d17501' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.61184756, 0.59663255, 0.59270396, 0.63115415, 0.63896226,\n",
       "        0.65908035, 0.64885917, 0.58448864, 0.59842909, 0.60899363,\n",
       "        0.58596779, 0.62819975, 0.61724216, 0.60834735, 0.574625  ,\n",
       "        0.58476676, 0.61064128, 0.57696646, 0.59166845, 0.61714374,\n",
       "        0.58690403, 0.58654231, 0.62181929, 0.62476231, 0.57697184,\n",
       "        0.62758047, 0.59648308, 0.59293281, 0.61720492, 0.6228372 ,\n",
       "        0.60541385, 0.56882178, 0.59896124, 0.61042328, 0.6461127 ,\n",
       "        0.64937743, 0.62866758, 0.65875721, 0.61553125, 0.62623856,\n",
       "        0.61696676, 0.6379526 , 0.65321174, 0.61956427, 0.62199991,\n",
       "        0.58920477, 0.58657059, 0.61709532, 0.59922674, 0.59304369,\n",
       "        0.64452154, 0.62048857, 0.58569903, 0.58691829, 0.60774859,\n",
       "        0.62235228, 0.57670261, 0.59134965, 0.57988754, 0.6121098 ,\n",
       "        0.6275914 , 0.623853  , 0.60819217, 0.58627665, 0.60689739,\n",
       "        0.60807319, 0.60429779, 0.60464341, 0.63758105, 0.62060121,\n",
       "        0.57642002, 0.61458702, 0.6264801 , 0.61672514, 0.58722116,\n",
       "        0.61474225, 0.6447234 , 0.63198057, 0.61573182, 0.56957362,\n",
       "        0.60757767, 0.64501733, 0.58375037, 0.5801384 , 0.63169275,\n",
       "        0.64618367, 0.66469218, 0.59291327, 0.62581671, 0.63963513,\n",
       "        0.6210701 , 0.59492448, 0.64436493, 0.59128377, 0.63258108,\n",
       "        0.58542898, 0.60670453, 0.59172848, 0.62562873, 0.60342113,\n",
       "...\n",
       "        0.62814479, 0.63560358, 0.62577275, 0.62917072, 0.60728057,\n",
       "        0.64694727, 0.64338901, 0.60885474, 0.64837448, 0.61288766,\n",
       "        0.61639513, 0.57296017, 0.63685425, 0.62112532, 0.61774486,\n",
       "        0.62788435, 0.5934597 , 0.62939623, 0.61377535, 0.60636678,\n",
       "        0.61435681, 0.62062337, 0.63874675, 0.61187091, 0.60098856,\n",
       "        0.62766924, 0.62518232, 0.59955492, 0.5837555 , 0.61653312,\n",
       "        0.61711366, 0.62854642, 0.61669393, 0.62434543, 0.62583575,\n",
       "        0.6460873 , 0.62313625, 0.61429421, 0.58809122, 0.62068378,\n",
       "        0.62514222, 0.60304098, 0.57596927, 0.62423764, 0.60257635,\n",
       "        0.61815005, 0.6112255 , 0.59709027, 0.60262638, 0.62710585,\n",
       "        0.59810464, 0.61517347, 0.61113644, 0.61224692, 0.61663315,\n",
       "        0.59297124, 0.59818281, 0.65937554, 0.62769856, 0.60659493,\n",
       "        0.56655874, 0.6196434 , 0.61076216, 0.61167796, 0.60559847,\n",
       "        0.60974646, 0.60760831, 0.63586841, 0.60486714, 0.60066776,\n",
       "        0.61111448, 0.57928402, 0.61097477, 0.63551966, 0.59425329,\n",
       "        0.59654978, 0.57701776, 0.66379553, 0.60474329, 0.5946529 ,\n",
       "        0.60946905, 0.5613821 , 0.60768842, 0.5970211 , 0.62568277,\n",
       "        0.60912376, 0.59346537, 0.61699138, 0.62209513, 0.60614322,\n",
       "        0.61555581, 0.61350092, 0.610368  , 0.59351572, 0.58839564,\n",
       "        0.62253613, 0.61667844, 0.62261733, 0.60282528, 0.6444416 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4536 0.4336 ... 0.4636 0.4284</div><input id='attrs-9def1fd3-a481-414c-bcec-c8a5e7a86bb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9def1fd3-a481-414c-bcec-c8a5e7a86bb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddf8ebe0-8017-4052-bea7-cb722fee156c' class='xr-var-data-in' type='checkbox'><label for='data-ddf8ebe0-8017-4052-bea7-cb722fee156c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.45360266, 0.43358008, 0.44284885, 0.4190654 , 0.45127289,\n",
       "        0.44056098, 0.40987997, 0.4161087 , 0.40867815, 0.3684835 ,\n",
       "        0.42789655, 0.42156931, 0.39325246, 0.46099414, 0.44194992,\n",
       "        0.46365549, 0.4413998 , 0.43287413, 0.42620511, 0.41959046,\n",
       "        0.41604708, 0.43279289, 0.44817191, 0.41745609, 0.42405745,\n",
       "        0.42125541, 0.41592506, 0.49016084, 0.44044432, 0.4905703 ,\n",
       "        0.44007234, 0.43655066, 0.4232082 , 0.43453163, 0.3988794 ,\n",
       "        0.44624136, 0.4578546 , 0.43957016, 0.44515701, 0.40016309,\n",
       "        0.45334317, 0.44905857, 0.4290796 , 0.4291329 , 0.45036777,\n",
       "        0.42070964, 0.44619395, 0.41890946, 0.41008225, 0.43098658,\n",
       "        0.43429607, 0.39821656, 0.43775519, 0.44956905, 0.45785082,\n",
       "        0.44771081, 0.42520618, 0.4111393 , 0.43383332, 0.41448599,\n",
       "        0.45367463, 0.43084889, 0.41576762, 0.42978193, 0.43159871,\n",
       "        0.44477954, 0.41208961, 0.42847193, 0.41645559, 0.41533656,\n",
       "        0.4140329 , 0.41699995, 0.41842454, 0.37525002, 0.42067161,\n",
       "        0.3853373 , 0.46767634, 0.42512371, 0.42291632, 0.4544366 ,\n",
       "        0.45332334, 0.43353346, 0.42043938, 0.42911782, 0.46011167,\n",
       "        0.44197158, 0.43189359, 0.41105617, 0.40732413, 0.41604688,\n",
       "        0.42875192, 0.44954459, 0.48508657, 0.42834394, 0.4400932 ,\n",
       "        0.40014532, 0.45529045, 0.44013069, 0.40763734, 0.42399618,\n",
       "...\n",
       "        0.43248392, 0.41154341, 0.40856903, 0.44001514, 0.43601335,\n",
       "        0.41310107, 0.44036387, 0.42223792, 0.44779243, 0.42093604,\n",
       "        0.41913477, 0.44717288, 0.45400988, 0.42782433, 0.45543649,\n",
       "        0.46191074, 0.4359715 , 0.41801724, 0.41282803, 0.43518462,\n",
       "        0.40893341, 0.38512417, 0.40777417, 0.44056146, 0.43441919,\n",
       "        0.43863874, 0.4538754 , 0.4471089 , 0.43319104, 0.43098764,\n",
       "        0.41193378, 0.41640987, 0.41590688, 0.40805931, 0.39778821,\n",
       "        0.42527272, 0.45031255, 0.42568834, 0.38727176, 0.43859684,\n",
       "        0.44827942, 0.4155939 , 0.44032002, 0.42827958, 0.41472156,\n",
       "        0.41677799, 0.39509825, 0.39681552, 0.45490523, 0.41115329,\n",
       "        0.42170363, 0.42823655, 0.41441553, 0.45699395, 0.43298275,\n",
       "        0.42861022, 0.42547235, 0.43463143, 0.44187749, 0.43820962,\n",
       "        0.43108498, 0.44531486, 0.41595366, 0.43020685, 0.43569946,\n",
       "        0.44288205, 0.43459647, 0.42690724, 0.41811777, 0.43098119,\n",
       "        0.41540598, 0.42986565, 0.44538902, 0.44560945, 0.41990353,\n",
       "        0.40397281, 0.42993807, 0.42489389, 0.41937636, 0.42989728,\n",
       "        0.43208538, 0.41692389, 0.40857434, 0.41811292, 0.40168247,\n",
       "        0.41162192, 0.42627835, 0.42453963, 0.40958193, 0.42839102,\n",
       "        0.41007365, 0.40569304, 0.43993127, 0.41848287, 0.42648024,\n",
       "        0.47057271, 0.40316632, 0.41209458, 0.46363767, 0.42838667]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d4d861a7-2f6b-4134-888b-7a448a36083a' class='xr-section-summary-in' type='checkbox'  ><label for='section-d4d861a7-2f6b-4134-888b-7a448a36083a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-82d18dc6-db33-4c56-b3cb-b9c82de162d4' class='xr-index-data-in' type='checkbox'/><label for='index-82d18dc6-db33-4c56-b3cb-b9c82de162d4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-17e114f8-3eb4-4580-8545-5433f94eb831' class='xr-index-data-in' type='checkbox'/><label for='index-17e114f8-3eb4-4580-8545-5433f94eb831' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76a7bd57-94e5-4e3c-86bf-6a4ad46c5e98' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76a7bd57-94e5-4e3c-86bf-6a4ad46c5e98' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-01-15T18:22:23.502516+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_dataa3761f73-593b-4c59-ba30-3fbd646e8edf\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_dataa3761f73-593b-4c59-ba30-3fbd646e8edf\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
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       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-item input:focus + label {\n",
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       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
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       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
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       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 57kB\n",
       "Dimensions:            (customer_id: 2357, obs_var: 2)\n",
       "Coordinates:\n",
       "  * customer_id        (customer_id) int64 19kB 1 2 3 4 ... 2354 2355 2356 2357\n",
       "  * obs_var            (obs_var) &lt;U9 72B &#x27;recency&#x27; &#x27;frequency&#x27;\n",
       "Data variables:\n",
       "    recency_frequency  (customer_id, obs_var) float64 38kB 49.0 3.0 ... 0.0 0.0\n",
       "Attributes:\n",
       "    created_at:                 2025-01-15T18:22:23.509273+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bf5fe040-a6cc-41c7-87a9-30bb6f7830a4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf5fe040-a6cc-41c7-87a9-30bb6f7830a4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>customer_id</span>: 2357</li><li><span class='xr-has-index'>obs_var</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b0bfa814-d345-47d3-a1d7-ad3b3dfd41c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0bfa814-d345-47d3-a1d7-ad3b3dfd41c9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>customer_id</span></div><div class='xr-var-dims'>(customer_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2354 2355 2356 2357</div><input id='attrs-c418cbbe-eeb6-4e46-816d-550a13eed4ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c418cbbe-eeb6-4e46-816d-550a13eed4ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56363535-54af-4524-a962-29d5275c7880' class='xr-var-data-in' type='checkbox'><label for='data-56363535-54af-4524-a962-29d5275c7880' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2355, 2356, 2357])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs_var</span></div><div class='xr-var-dims'>(obs_var)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;recency&#x27; &#x27;frequency&#x27;</div><input id='attrs-55454562-2794-4691-b9be-d6bb1512e7ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55454562-2794-4691-b9be-d6bb1512e7ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02728810-ff0f-4648-90a2-65b4ccbd5fee' class='xr-var-data-in' type='checkbox'><label for='data-02728810-ff0f-4648-90a2-65b4ccbd5fee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;recency&#x27;, &#x27;frequency&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65fd3859-4bd6-4416-ac7d-216cc16d51d2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-65fd3859-4bd6-4416-ac7d-216cc16d51d2' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>recency_frequency</span></div><div class='xr-var-dims'>(customer_id, obs_var)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.0 3.0 2.0 1.0 ... 6.0 0.0 0.0</div><input id='attrs-09bab4fb-2103-43fe-aad4-d26620376954' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09bab4fb-2103-43fe-aad4-d26620376954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb40a3d0-095b-42b6-80db-af4366159286' class='xr-var-data-in' type='checkbox'><label for='data-cb40a3d0-095b-42b6-80db-af4366159286' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[49.,  3.],\n",
       "       [ 2.,  1.],\n",
       "       [ 0.,  0.],\n",
       "       ...,\n",
       "       [44.,  1.],\n",
       "       [62.,  6.],\n",
       "       [ 0.,  0.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c41eae6-0524-4eba-a66f-baa8d74b9f78' class='xr-section-summary-in' type='checkbox'  ><label for='section-1c41eae6-0524-4eba-a66f-baa8d74b9f78' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>customer_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-376fad29-ec6a-4219-854d-69cf4d7162b6' class='xr-index-data-in' type='checkbox'/><label for='index-376fad29-ec6a-4219-854d-69cf4d7162b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "       ...\n",
       "       2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;customer_id&#x27;, length=2357))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs_var</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-33cf25ff-211d-4955-9a58-b7cb6f35939d' class='xr-index-data-in' type='checkbox'/><label for='index-33cf25ff-211d-4955-9a58-b7cb6f35939d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;recency&#x27;, &#x27;frequency&#x27;], dtype=&#x27;object&#x27;, name=&#x27;obs_var&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4677dae6-4f96-46a6-a79e-1b0a583f5ae0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4677dae6-4f96-46a6-a79e-1b0a583f5ae0' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-01-15T18:22:23.509273+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd></dl></div></li></ul></div></div><br></div>\n",
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       "                  <div class=\"xr-section-details\">\n",
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       ".xr-wrap {\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       "\n",
       ".xr-header {\n",
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       "  margin-bottom: 4px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
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       "\n",
       ".xr-obj-type,\n",
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       "  margin-left: 2px;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 94kB\n",
       "Dimensions:      (index: 2357)\n",
       "Coordinates:\n",
       "  * index        (index) int64 19kB 0 1 2 3 4 5 ... 2352 2353 2354 2355 2356\n",
       "Data variables:\n",
       "    customer_id  (index) int64 19kB 1 2 3 4 5 6 ... 2353 2354 2355 2356 2357\n",
       "    frequency    (index) float64 19kB 3.0 1.0 0.0 0.0 0.0 ... 5.0 1.0 6.0 0.0\n",
       "    recency      (index) float64 19kB 49.0 2.0 0.0 0.0 ... 24.0 44.0 62.0 0.0\n",
       "    T            (index) float64 19kB 78.0 78.0 78.0 78.0 ... 66.0 66.0 66.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-85feb54f-997e-4108-881f-eb74e9ad24cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85feb54f-997e-4108-881f-eb74e9ad24cd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>index</span>: 2357</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fefd5894-97a5-45f5-b848-e2c07a4c2430' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fefd5894-97a5-45f5-b848-e2c07a4c2430' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2353 2354 2355 2356</div><input id='attrs-7a3d34cc-98fd-46ec-9dbf-033779eb457f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a3d34cc-98fd-46ec-9dbf-033779eb457f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdd5074b-7fa5-44ae-8944-8a31eb21890d' class='xr-var-data-in' type='checkbox'><label for='data-fdd5074b-7fa5-44ae-8944-8a31eb21890d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2354, 2355, 2356])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8184cd75-fe26-4139-95ed-44dc75e73df1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8184cd75-fe26-4139-95ed-44dc75e73df1' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>customer_id</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2354 2355 2356 2357</div><input id='attrs-ac648ceb-7f21-4aad-bb7f-7940a30614dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac648ceb-7f21-4aad-bb7f-7940a30614dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fb644f5-f9f1-4a5e-bf71-9ec64cdc85e4' class='xr-var-data-in' type='checkbox'><label for='data-3fb644f5-f9f1-4a5e-bf71-9ec64cdc85e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2355, 2356, 2357])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 1.0 0.0 0.0 ... 5.0 1.0 6.0 0.0</div><input id='attrs-53d084bd-17ad-4e33-a6a6-02b0d1833362' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53d084bd-17ad-4e33-a6a6-02b0d1833362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1c13957-9d48-445e-bfc2-19902bf5c226' class='xr-var-data-in' type='checkbox'><label for='data-e1c13957-9d48-445e-bfc2-19902bf5c226' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3., 1., 0., ..., 1., 6., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.0 2.0 0.0 0.0 ... 44.0 62.0 0.0</div><input id='attrs-71459a8c-612a-42dd-b2d9-b5bc58440a9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71459a8c-612a-42dd-b2d9-b5bc58440a9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ea764aa-251f-4150-965e-cc9c3ddd2c8d' class='xr-var-data-in' type='checkbox'><label for='data-6ea764aa-251f-4150-965e-cc9c3ddd2c8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([49.,  2.,  0., ..., 44., 62.,  0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>78.0 78.0 78.0 ... 66.0 66.0 66.0</div><input id='attrs-545b70fa-1760-4d1a-8dfd-efc44cf4120f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-545b70fa-1760-4d1a-8dfd-efc44cf4120f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5f441e6-71ff-4d88-8aa2-49740ffe1e23' class='xr-var-data-in' type='checkbox'><label for='data-e5f441e6-71ff-4d88-8aa2-49740ffe1e23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([78., 78., 78., ..., 66., 66., 66.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-461fd720-5ab6-4452-aa1d-929b0759f3f8' class='xr-section-summary-in' type='checkbox'  ><label for='section-461fd720-5ab6-4452-aa1d-929b0759f3f8' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>index</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0e13c6ec-5c8f-4c60-884f-2fb6614023a5' class='xr-index-data-in' type='checkbox'/><label for='index-0e13c6ec-5c8f-4c60-884f-2fb6614023a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=2357, step=1, name=&#x27;index&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-abf4b4ac-7496-467b-b494-f23e04051569' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-abf4b4ac-7496-467b-b494-f23e04051569' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
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       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
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       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
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       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "\n",
       ".xr-sections.group-sections {\n",
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> observed_data\n",
       "\t> fit_data"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnbd_advi = clv.ParetoNBDModel(data=rfm_data)\n",
    "pnbd_advi.fit(\n",
    "    n=12500,\n",
    "    fit_method=\"advi\", \n",
    "    obj_n_mc=15,\n",
    "    # obj_optimizer=pm.adagrad(learning_rate=100.),\n",
    "    idata_kwargs={\"log_likelihood\": True}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "arviz - WARNING - Shape validation failed: input_shape: (1, 500), minimum_shape: (chains=2, draws=4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>15.547</td>\n",
       "      <td>0.567</td>\n",
       "      <td>14.514</td>\n",
       "      <td>16.599</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.021</td>\n",
       "      <td>366.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>12.531</td>\n",
       "      <td>1.160</td>\n",
       "      <td>10.681</td>\n",
       "      <td>14.933</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.038</td>\n",
       "      <td>458.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.612</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>448.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.429</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>502.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha  15.547  0.567  14.514   16.599      0.030    0.021     366.0     453.0   \n",
       "beta   12.531  1.160  10.681   14.933      0.054    0.038     458.0     372.0   \n",
       "r       0.612  0.019   0.575    0.646      0.001    0.001     448.0     353.0   \n",
       "s       0.429  0.021   0.396    0.472      0.001    0.001     502.0     446.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    NaN  \n",
       "beta     NaN  \n",
       "r        NaN  \n",
       "s        NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnbd_advi.fit_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(pnbd_advi.approx.hist, )\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Full-rank fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "Output()"
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      "Finished [100%]: Average Loss = 16,530\n"
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20kB\n",
       "Dimensions:  (chain: 1, draw: 500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 8B 0\n",
       "  * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "Data variables:\n",
       "    alpha    (chain, draw) float64 4kB 17.41 12.11 15.68 ... 13.78 13.21 25.11\n",
       "    beta     (chain, draw) float64 4kB 27.5 19.56 20.12 ... 4.865 3.775 9.99\n",
       "    r        (chain, draw) float64 4kB 0.5248 0.3552 0.6851 ... 0.7622 0.7358\n",
       "    s        (chain, draw) float64 4kB 0.5267 0.3196 0.5446 ... 0.2275 0.3081\n",
       "Attributes:\n",
       "    created_at:                 2025-01-15T19:11:38.027406+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4cc230a2-7b74-4f81-9ede-6a7053daa7ef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4cc230a2-7b74-4f81-9ede-6a7053daa7ef' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1c7a84ea-4316-43c2-971e-ccfb72e8c200' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c7a84ea-4316-43c2-971e-ccfb72e8c200' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-bf15799c-456e-44d1-b91c-10793474ee7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf15799c-456e-44d1-b91c-10793474ee7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37188ddb-473f-48b1-8aad-9abba587d886' class='xr-var-data-in' type='checkbox'><label for='data-37188ddb-473f-48b1-8aad-9abba587d886' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-fd4bdc79-641c-4916-9972-a7cefe5d5512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd4bdc79-641c-4916-9972-a7cefe5d5512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c71b1bef-7d09-452d-bf1b-ac3ece144508' class='xr-var-data-in' type='checkbox'><label for='data-c71b1bef-7d09-452d-bf1b-ac3ece144508' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e22bd97f-3b1e-4ee5-8247-e3cc9edb334f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e22bd97f-3b1e-4ee5-8247-e3cc9edb334f' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.41 12.11 15.68 ... 13.21 25.11</div><input id='attrs-e9f67111-9e98-4c3c-85a6-348fb385e354' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9f67111-9e98-4c3c-85a6-348fb385e354' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c78794f3-5709-42f2-be46-26a480600a58' class='xr-var-data-in' type='checkbox'><label for='data-c78794f3-5709-42f2-be46-26a480600a58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[17.40732857, 12.11472976, 15.68158164,  6.9693671 , 12.59738939,\n",
       "         9.70865302, 15.23242759, 10.90988983, 16.77949716, 29.14462267,\n",
       "        13.82890353, 26.00418683, 15.26678075,  9.6107144 , 12.21529828,\n",
       "        14.74474619, 17.51976856, 19.20507969, 16.78843214, 10.88333241,\n",
       "        22.01698418, 16.1399767 , 10.40600649, 12.82578101, 22.69444991,\n",
       "        12.33231233, 12.71172641, 13.8814525 , 13.41420359, 10.29556949,\n",
       "        20.44245063, 14.23122525, 11.24799436,  8.54022869, 15.00302242,\n",
       "        12.87045943, 13.58483561, 16.13274271, 11.55540468, 20.40266959,\n",
       "        11.12850389, 10.45088471, 16.87403876, 11.42532379, 10.63992305,\n",
       "        33.53810525, 13.99074028, 18.30503687, 24.65860919, 15.65290194,\n",
       "        10.5080416 , 14.29529369,  9.78866412, 15.11906473, 11.36565559,\n",
       "        10.80412384, 25.46288039, 13.93594279, 11.0316977 , 16.29235783,\n",
       "        13.79746381, 11.09851249, 12.86089397, 15.82064064, 12.63101753,\n",
       "        14.86057571, 14.90989565, 22.07531232,  9.36140366, 11.59771911,\n",
       "        21.42052273, 19.53090006, 12.22358172, 20.46958029, 12.93879504,\n",
       "        14.29780243, 12.0415928 , 20.77783859, 13.63134259, 17.09699483,\n",
       "        18.65562066, 16.6672051 ,  9.9619667 , 21.39668177, 22.11291064,\n",
       "        10.10131499, 17.60017884, 10.95622277,  9.30372878, 20.3549573 ,\n",
       "        15.79682067, 11.39401871,  8.84379532, 18.56335014, 21.96637042,\n",
       "         9.15598396, 14.15597708, 16.8628024 , 14.17675566, 16.17182663,\n",
       "...\n",
       "        24.38833533, 12.60102389, 13.01945368, 14.16085036, 13.66555376,\n",
       "        16.98707707, 12.76824266, 12.49343174, 13.52423638, 12.00223595,\n",
       "         9.24085812, 16.2853849 , 15.60045132, 18.1010046 , 26.30416794,\n",
       "        16.57487327, 18.15764001, 15.27535084, 11.04550152, 21.73225071,\n",
       "        18.49955151,  8.45509585, 11.5696704 ,  6.14124521, 21.25177611,\n",
       "         9.708234  , 15.18899654, 17.06419218, 25.69827234,  6.74222438,\n",
       "        10.42046361, 12.88988196, 18.81058195, 20.11893507, 15.54732997,\n",
       "        14.37705576, 25.78238652, 19.48658923, 10.36302541, 14.9122159 ,\n",
       "        15.9557173 , 11.53434233, 17.21463732, 16.70659667,  6.80459704,\n",
       "        17.85503275, 16.55169416, 11.95714257, 17.28375582, 17.51094315,\n",
       "        12.67977865, 17.39812911, 16.34906248,  7.50015659, 32.51248786,\n",
       "        12.16702807, 13.41440306, 18.66372423, 21.4028922 , 20.09856403,\n",
       "        13.85103722, 17.51199807, 11.7263122 , 14.77936557, 10.58907005,\n",
       "        30.47039624, 15.76502375, 16.08123008, 11.83871516, 16.11658254,\n",
       "        11.8787397 , 12.07107954, 14.40326619, 31.78433562, 14.9771823 ,\n",
       "        23.66584321, 10.1466839 , 13.48505891, 20.79671791, 12.47776057,\n",
       "        15.30556134,  9.29259073, 20.67634495, 15.11531659, 13.57017063,\n",
       "        15.20202662, 17.41622619, 15.94687162, 20.61810996,  6.05670502,\n",
       "        11.95667686, 28.27588056, 28.29646322, 25.99940984, 22.16803604,\n",
       "        10.94999218,  9.09917598, 13.78090897, 13.20542254, 25.11157618]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.5 19.56 20.12 ... 3.775 9.99</div><input id='attrs-5bf4859e-0c77-4dc7-8870-de46e1ddff2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bf4859e-0c77-4dc7-8870-de46e1ddff2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2847b05a-a136-46fd-b2cb-c1318b644ace' class='xr-var-data-in' type='checkbox'><label for='data-2847b05a-a136-46fd-b2cb-c1318b644ace' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[27.49592142, 19.56290104, 20.1172915 , 22.9023042 ,  5.02026138,\n",
       "         6.63569735, 15.25090704, 26.9054801 ,  9.74147009,  4.66766409,\n",
       "        18.79564305,  6.85893152, 22.62139425, 16.77671507,  6.90441197,\n",
       "        12.46477727,  4.95052816,  3.64196142,  9.36934864, 22.92795134,\n",
       "         9.63990236, 13.87796077, 12.37220291,  6.10728385,  9.69333874,\n",
       "        10.44330249,  8.6546967 , 33.22681214,  9.0905623 ,  9.08699428,\n",
       "         4.68115667, 24.29110276, 22.45796562, 11.5019248 ,  8.3265831 ,\n",
       "        18.72722398, 14.09877897,  6.3278534 , 10.05372017, 16.86696951,\n",
       "         4.30625815, 13.72405376, 11.22207351, 14.08356606, 18.31234769,\n",
       "        24.20957075, 22.98894379, 12.98738515,  8.16519098, 24.54087597,\n",
       "        10.89908209, 12.54647879, 10.86817218,  9.09093098,  9.27406751,\n",
       "        11.03550755,  6.48896289, 16.68474661, 18.83966991, 22.99233993,\n",
       "         8.54266337, 15.97067403, 18.64931738, 21.12736003, 17.90833035,\n",
       "         7.57791875,  6.01013504,  3.22536461, 15.05640175, 10.69796637,\n",
       "         8.06682658, 19.95131851,  5.6320883 , 10.04397346,  6.89139169,\n",
       "        15.61393714, 10.91110662, 52.29164809, 16.92803989,  5.97938866,\n",
       "         4.10257855,  8.89959928, 15.49905481, 17.04873551, 13.12265038,\n",
       "        13.64076411,  8.20937085,  9.21358744, 12.76114939,  5.98428388,\n",
       "         4.4713564 , 16.3217472 ,  8.32397506, 15.78677162,  6.11732052,\n",
       "        10.68466044, 13.8068539 , 27.06020122, 15.40872945, 16.29735772,\n",
       "...\n",
       "         6.71439172, 34.96645343, 10.5384091 , 13.4857863 , 17.53832298,\n",
       "         5.45671193, 37.14881817, 18.53754401, 25.38464793, 16.22904992,\n",
       "         3.17654242,  4.00912519, 15.82834871, 14.60752126, 11.91951066,\n",
       "         3.64291523,  2.59345191,  9.1445653 , 15.19623797, 10.00791024,\n",
       "         5.81766879,  6.58395587, 36.59066421, 10.8748795 , 13.9435076 ,\n",
       "        34.47254828, 15.49609163, 48.92626211,  9.96566425,  7.68455859,\n",
       "         6.24889964,  6.32318683, 10.80657636,  8.29128303, 18.33291536,\n",
       "        20.95569122,  4.34035436,  7.38553715, 12.77409535, 14.83938153,\n",
       "         4.02783268, 10.951983  , 10.12002245, 15.09339586,  8.53964981,\n",
       "        10.02083664,  8.4844364 , 17.91772293, 16.9920743 , 12.09566729,\n",
       "        17.28270573,  9.01728317, 22.16120369, 13.6356762 , 23.26603988,\n",
       "         8.62229744, 10.38861888, 36.4443709 , 11.77839901, 27.30822521,\n",
       "        16.19004108,  6.34301118, 24.84621102,  4.09824165, 58.77193524,\n",
       "         6.17902364, 18.63962858, 10.7216831 ,  5.16450763,  9.33536735,\n",
       "        20.40556891,  1.71697514,  5.55989087,  5.7722524 , 15.59568224,\n",
       "        24.27946636,  3.49971629,  8.09756756,  9.71759195, 13.94059024,\n",
       "        27.5785291 , 40.03191909, 22.27386448, 25.29019295, 37.36822884,\n",
       "        30.56225829, 21.1245795 , 10.45320561,  4.1995309 , 17.58221981,\n",
       "        15.89872659,  5.57644369, 14.2841248 , 16.46310941,  6.49800651,\n",
       "        11.89909724, 29.3369912 ,  4.86494106,  3.77461823,  9.99033201]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5248 0.3552 ... 0.7622 0.7358</div><input id='attrs-d903e36a-ca96-405d-8f1e-fcb78b01f6fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d903e36a-ca96-405d-8f1e-fcb78b01f6fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14835c6b-bd08-406e-929b-f2de6e69465f' class='xr-var-data-in' type='checkbox'><label for='data-14835c6b-bd08-406e-929b-f2de6e69465f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.52480293, 0.35523093, 0.68508645, 0.37331591, 0.40658807,\n",
       "        0.65368196, 0.9914236 , 0.38168483, 0.70562711, 1.11260739,\n",
       "        0.62384997, 0.72456166, 0.41858473, 0.36208905, 0.44425437,\n",
       "        0.43750646, 0.71168543, 0.8950023 , 0.72940919, 0.34049697,\n",
       "        0.72739557, 0.73715138, 0.51400064, 0.36428354, 0.91334803,\n",
       "        0.50210946, 0.66026297, 0.47548816, 0.63482095, 0.40588665,\n",
       "        0.75873013, 0.53652493, 0.50420639, 0.53866864, 0.42833735,\n",
       "        0.75137833, 0.87824072, 0.57593859, 0.41087254, 0.71772875,\n",
       "        0.5305868 , 0.44116634, 0.69572164, 0.54427605, 0.38192694,\n",
       "        1.08808204, 0.62760991, 0.84348728, 0.75550236, 0.28317998,\n",
       "        0.30378767, 0.44957884, 0.5173591 , 0.45304084, 0.40126991,\n",
       "        0.47558697, 1.06620442, 0.40998209, 0.41694923, 0.47099914,\n",
       "        0.47882361, 0.32528773, 0.42472644, 0.68986688, 0.50275578,\n",
       "        0.54719235, 0.66179106, 0.85857011, 0.44301317, 0.62593217,\n",
       "        0.60682449, 0.44908721, 0.43721456, 0.61381567, 0.77691527,\n",
       "        0.75246786, 0.37885552, 0.69381797, 0.56665245, 0.84255304,\n",
       "        0.86925046, 0.52670303, 0.35798754, 0.94752553, 0.81485972,\n",
       "        0.41905948, 0.73535806, 0.43357685, 0.35967847, 0.89601675,\n",
       "        0.77349095, 0.35842299, 0.37369881, 0.67352544, 0.89636143,\n",
       "        0.25332254, 0.64248286, 0.48436388, 0.4416575 , 0.7841683 ,\n",
       "...\n",
       "        0.79748972, 0.31331136, 0.80338095, 0.56755437, 0.37541718,\n",
       "        1.03633277, 0.51051682, 0.56695112, 0.39682813, 0.41271359,\n",
       "        0.43824213, 0.4431798 , 0.47139291, 0.57464741, 0.95435325,\n",
       "        0.70385088, 0.61206175, 0.65873078, 0.39924309, 0.97300961,\n",
       "        0.85099849, 0.31894034, 0.58096076, 0.30055049, 0.63545694,\n",
       "        0.35805608, 0.58250566, 0.75150141, 0.73330571, 0.30034408,\n",
       "        0.47343854, 0.3788778 , 1.0429381 , 0.8229819 , 0.55722595,\n",
       "        0.51977826, 1.02550091, 0.57941196, 0.43124151, 0.65607524,\n",
       "        0.92619139, 0.46431477, 1.01139073, 0.80910911, 0.36191032,\n",
       "        0.64109933, 0.6002045 , 0.56331226, 0.85602434, 0.65359802,\n",
       "        0.53194947, 0.54227403, 0.48549545, 0.38787539, 1.2415347 ,\n",
       "        0.49605273, 0.46707195, 0.78630279, 0.79761776, 0.95786542,\n",
       "        0.55387605, 0.87823227, 0.42071974, 0.61756653, 0.36122999,\n",
       "        1.29015515, 0.58260598, 0.58562123, 0.41382938, 0.66920965,\n",
       "        0.5093699 , 0.59094092, 0.54412729, 1.14106833, 0.66054062,\n",
       "        0.83972345, 0.36171787, 0.73927649, 0.82989113, 0.48513967,\n",
       "        0.74887306, 0.32083504, 0.76399596, 0.73261986, 0.59521286,\n",
       "        0.72495959, 0.54531268, 0.74508448, 0.82378955, 0.22948639,\n",
       "        0.55584819, 1.05904991, 0.91157202, 1.30692409, 0.90042842,\n",
       "        0.50253543, 0.4139026 , 0.71865453, 0.76219224, 0.73577498]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5267 0.3196 ... 0.2275 0.3081</div><input id='attrs-74ccaf27-cfec-47ae-85ef-0ff461274659' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74ccaf27-cfec-47ae-85ef-0ff461274659' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-137dfc7a-9105-484e-bcdf-5add9f6e986b' class='xr-var-data-in' type='checkbox'><label for='data-137dfc7a-9105-484e-bcdf-5add9f6e986b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.52671238, 0.31959895, 0.54456633, 0.47578506, 0.17063782,\n",
       "        0.65214308, 0.55574779, 0.34767447, 0.50101181, 0.23029964,\n",
       "        0.47708207, 0.26111044, 0.24761171, 0.40254823, 0.19685203,\n",
       "        0.32988545, 0.2523249 , 0.21492184, 0.31949881, 0.38845304,\n",
       "        0.46618708, 0.56659939, 0.69353756, 0.13058085, 0.3388846 ,\n",
       "        0.34149601, 0.29271553, 0.45422388, 0.51110532, 0.24580273,\n",
       "        0.26094007, 0.64501925, 0.68891516, 0.42994501, 0.18306314,\n",
       "        1.02562655, 0.70025771, 0.22831719, 0.23489214, 0.51732082,\n",
       "        0.31519127, 0.32241045, 0.34057615, 0.61016189, 0.58138025,\n",
       "        0.81628919, 0.47776774, 0.56606467, 0.34954135, 0.29501947,\n",
       "        0.35348769, 0.23439398, 0.264407  , 0.24757632, 0.24050966,\n",
       "        0.27018127, 0.28412909, 0.28185527, 0.24666074, 0.56321024,\n",
       "        0.32926734, 0.35539489, 0.30465925, 0.7498847 , 0.35450766,\n",
       "        0.26702746, 0.34601497, 0.17398201, 0.3945864 , 0.38864034,\n",
       "        0.28535183, 0.31456415, 0.14270478, 0.31018652, 0.52411012,\n",
       "        0.67571593, 0.2402899 , 0.55143609, 0.77748972, 0.39046399,\n",
       "        0.39547958, 0.22044325, 0.27160597, 0.70408819, 0.47433835,\n",
       "        0.42410471, 0.31862839, 0.22987257, 0.36766547, 0.25127318,\n",
       "        0.36807226, 0.38277405, 0.23341354, 0.73867191, 0.37790735,\n",
       "        0.29301793, 0.6368385 , 0.4300432 , 0.36820282, 0.54073703,\n",
       "...\n",
       "        0.24909319, 0.39897381, 0.40068932, 0.41039307, 0.50021097,\n",
       "        0.6518159 , 0.66121954, 0.35272845, 0.39409524, 0.24506085,\n",
       "        0.25991924, 0.25593887, 0.2976879 , 0.40599405, 0.57713304,\n",
       "        0.28110315, 0.11928017, 0.40710944, 0.29808252, 0.43203581,\n",
       "        0.61417366, 0.24931684, 0.94213504, 0.24385905, 0.29649052,\n",
       "        0.374426  , 0.60855879, 0.76690589, 0.27809848, 0.27236554,\n",
       "        0.23822543, 0.30604886, 0.92132697, 0.40408832, 0.26094171,\n",
       "        0.5539281 , 0.2373192 , 0.22944435, 0.48588626, 0.59077894,\n",
       "        0.38183554, 0.27687894, 0.51945556, 0.55002744, 0.27671584,\n",
       "        0.35669061, 0.24683886, 0.63602418, 0.84564295, 0.40691685,\n",
       "        0.41938517, 0.23874071, 0.27338761, 0.40483411, 0.55559518,\n",
       "        0.18568873, 0.34915469, 0.97180554, 0.40331975, 0.62075355,\n",
       "        0.61185625, 0.40888807, 0.58734875, 0.24097282, 0.78179369,\n",
       "        0.29038628, 0.50056152, 0.27325325, 0.24109463, 0.30982258,\n",
       "        0.37929018, 0.14354176, 0.21264799, 0.3814795 , 0.51749334,\n",
       "        0.45886657, 0.20047955, 0.85968954, 0.33750743, 0.36600228,\n",
       "        0.64820476, 0.71963668, 0.81893774, 0.81670955, 0.6591026 ,\n",
       "        1.00706427, 0.50106708, 0.57023441, 0.28246564, 0.23465534,\n",
       "        0.32735005, 0.27491872, 0.51375722, 0.38245761, 0.41464345,\n",
       "        0.40532064, 0.55818721, 0.3549923 , 0.22752872, 0.3080575 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2739bf45-62c6-4f7c-993a-9712bd9c8775' class='xr-section-summary-in' type='checkbox'  ><label for='section-2739bf45-62c6-4f7c-993a-9712bd9c8775' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8b6b8a8c-bf00-40a2-90eb-7d76391fcab7' class='xr-index-data-in' type='checkbox'/><label for='index-8b6b8a8c-bf00-40a2-90eb-7d76391fcab7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f52f0513-a8e2-435f-ae90-753e0a6e7480' class='xr-index-data-in' type='checkbox'/><label for='index-f52f0513-a8e2-435f-ae90-753e0a6e7480' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7461bd0-0b42-4045-8bb6-bae1afcf2e2d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7461bd0-0b42-4045-8bb6-bae1afcf2e2d' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-01-15T19:11:38.027406+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_data74989076-d0f3-42b3-be5d-afe359fc5c9e\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data74989076-d0f3-42b3-be5d-afe359fc5c9e\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1f1f1f;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
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       "\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "\n",
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       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
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       ".xr-var-dims,\n",
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       "\n",
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       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
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       "\n",
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       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 57kB\n",
       "Dimensions:            (customer_id: 2357, obs_var: 2)\n",
       "Coordinates:\n",
       "  * customer_id        (customer_id) int64 19kB 1 2 3 4 ... 2354 2355 2356 2357\n",
       "  * obs_var            (obs_var) &lt;U9 72B &#x27;recency&#x27; &#x27;frequency&#x27;\n",
       "Data variables:\n",
       "    recency_frequency  (customer_id, obs_var) float64 38kB 49.0 3.0 ... 0.0 0.0\n",
       "Attributes:\n",
       "    created_at:                 2025-01-15T19:11:38.033900+00:00\n",
       "    arviz_version:              0.20.0\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.20.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fd26b874-e635-453d-8315-60515e616290' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fd26b874-e635-453d-8315-60515e616290' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>customer_id</span>: 2357</li><li><span class='xr-has-index'>obs_var</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8b12108b-0b8e-4c30-9ebc-88f3b59ce1ca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b12108b-0b8e-4c30-9ebc-88f3b59ce1ca' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>customer_id</span></div><div class='xr-var-dims'>(customer_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2354 2355 2356 2357</div><input id='attrs-394319ab-478e-426e-9e27-f2e18ea2cf0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-394319ab-478e-426e-9e27-f2e18ea2cf0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b490a444-c674-4d77-80e2-b2834708750e' class='xr-var-data-in' type='checkbox'><label for='data-b490a444-c674-4d77-80e2-b2834708750e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2355, 2356, 2357])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs_var</span></div><div class='xr-var-dims'>(obs_var)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;recency&#x27; &#x27;frequency&#x27;</div><input id='attrs-4b9a0fec-cd6f-4900-8f8f-071a477c8042' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b9a0fec-cd6f-4900-8f8f-071a477c8042' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4829274b-07a5-4a94-85e4-6369335817ce' class='xr-var-data-in' type='checkbox'><label for='data-4829274b-07a5-4a94-85e4-6369335817ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;recency&#x27;, &#x27;frequency&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97d914a2-63f3-45e6-8c04-dac6faa0e9fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97d914a2-63f3-45e6-8c04-dac6faa0e9fd' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>recency_frequency</span></div><div class='xr-var-dims'>(customer_id, obs_var)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.0 3.0 2.0 1.0 ... 6.0 0.0 0.0</div><input id='attrs-79f427a8-0605-471a-abf2-7a657692dad5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79f427a8-0605-471a-abf2-7a657692dad5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9216b568-6350-4b43-a65a-144e0b3aa3f2' class='xr-var-data-in' type='checkbox'><label for='data-9216b568-6350-4b43-a65a-144e0b3aa3f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[49.,  3.],\n",
       "       [ 2.,  1.],\n",
       "       [ 0.,  0.],\n",
       "       ...,\n",
       "       [44.,  1.],\n",
       "       [62.,  6.],\n",
       "       [ 0.,  0.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a8c96b1-d40c-4d69-b7bc-9b3cc779cfe3' class='xr-section-summary-in' type='checkbox'  ><label for='section-1a8c96b1-d40c-4d69-b7bc-9b3cc779cfe3' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>customer_id</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a0d5a5a2-e8bd-49e8-a8e8-31ef6165cc73' class='xr-index-data-in' type='checkbox'/><label for='index-a0d5a5a2-e8bd-49e8-a8e8-31ef6165cc73' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "       ...\n",
       "       2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;customer_id&#x27;, length=2357))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs_var</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7e012272-3370-46c2-9fe7-99c908581c0c' class='xr-index-data-in' type='checkbox'/><label for='index-7e012272-3370-46c2-9fe7-99c908581c0c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;recency&#x27;, &#x27;frequency&#x27;], dtype=&#x27;object&#x27;, name=&#x27;obs_var&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-668cc5b0-0078-4b04-ba09-4afc128057af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-668cc5b0-0078-4b04-ba09-4afc128057af' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-01-15T19:11:38.033900+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.20.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.20.0</dd></dl></div></li></ul></div></div><br></div>\n",
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       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 94kB\n",
       "Dimensions:      (index: 2357)\n",
       "Coordinates:\n",
       "  * index        (index) int64 19kB 0 1 2 3 4 5 ... 2352 2353 2354 2355 2356\n",
       "Data variables:\n",
       "    customer_id  (index) int64 19kB 1 2 3 4 5 6 ... 2353 2354 2355 2356 2357\n",
       "    frequency    (index) float64 19kB 3.0 1.0 0.0 0.0 0.0 ... 5.0 1.0 6.0 0.0\n",
       "    recency      (index) float64 19kB 49.0 2.0 0.0 0.0 ... 24.0 44.0 62.0 0.0\n",
       "    T            (index) float64 19kB 78.0 78.0 78.0 78.0 ... 66.0 66.0 66.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-892174d9-557a-4bb2-b410-03185ed69886' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-892174d9-557a-4bb2-b410-03185ed69886' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>index</span>: 2357</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-48e7aaab-f164-44d1-9b2c-b0c4017b5f61' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48e7aaab-f164-44d1-9b2c-b0c4017b5f61' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2353 2354 2355 2356</div><input id='attrs-63d62c74-e3da-4707-bf25-54038c96f694' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63d62c74-e3da-4707-bf25-54038c96f694' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ece0217-11db-4b4f-8f69-571809b99d64' class='xr-var-data-in' type='checkbox'><label for='data-7ece0217-11db-4b4f-8f69-571809b99d64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2354, 2355, 2356])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-82524343-71ce-4ed7-932e-e7ad6d7c76fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82524343-71ce-4ed7-932e-e7ad6d7c76fa' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>customer_id</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2354 2355 2356 2357</div><input id='attrs-14319a0d-6de4-470a-8364-1d8b153f48be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14319a0d-6de4-470a-8364-1d8b153f48be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2eda2a8-4bc8-4de6-99e8-239f39951be7' class='xr-var-data-in' type='checkbox'><label for='data-f2eda2a8-4bc8-4de6-99e8-239f39951be7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2355, 2356, 2357])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 1.0 0.0 0.0 ... 5.0 1.0 6.0 0.0</div><input id='attrs-da450077-3680-4eb9-a45a-8d2579494f34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da450077-3680-4eb9-a45a-8d2579494f34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a22327c5-a3f6-4415-b445-75cea61b7262' class='xr-var-data-in' type='checkbox'><label for='data-a22327c5-a3f6-4415-b445-75cea61b7262' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3., 1., 0., ..., 1., 6., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>49.0 2.0 0.0 0.0 ... 44.0 62.0 0.0</div><input id='attrs-a9ea2d50-c82a-4f07-a619-095f10f96284' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9ea2d50-c82a-4f07-a619-095f10f96284' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7403bfa-ab19-44a0-8345-61884f380a0b' class='xr-var-data-in' type='checkbox'><label for='data-c7403bfa-ab19-44a0-8345-61884f380a0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([49.,  2.,  0., ..., 44., 62.,  0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>78.0 78.0 78.0 ... 66.0 66.0 66.0</div><input id='attrs-e10e6cc9-c06d-495b-b742-318f8022581b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e10e6cc9-c06d-495b-b742-318f8022581b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f12ff246-579c-4e82-9be2-1a21407eafa7' class='xr-var-data-in' type='checkbox'><label for='data-f12ff246-579c-4e82-9be2-1a21407eafa7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([78., 78., 78., ..., 66., 66., 66.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c91d3b2-1b5e-4290-9dee-b98fdef6d6a7' class='xr-section-summary-in' type='checkbox'  ><label for='section-5c91d3b2-1b5e-4290-9dee-b98fdef6d6a7' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>index</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7dbf328b-83d2-4d42-9362-4fccbfd3548f' class='xr-index-data-in' type='checkbox'/><label for='index-7dbf328b-83d2-4d42-9362-4fccbfd3548f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=2357, step=1, name=&#x27;index&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14366b86-41c5-4a18-90f3-3ab6e27b9e49' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-14366b86-41c5-4a18-90f3-3ab6e27b9e49' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
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       "\n",
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       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> observed_data\n",
       "\t> fit_data"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnbd_fullrank = clv.ParetoNBDModel(data=rfm_data)\n",
    "pnbd_fullrank.fit(\n",
    "    fit_method=\"fullrank_advi\", \n",
    "    obj_n_mc=5,\n",
    "    idata_kwargs={\"log_likelihood\": True}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "arviz - WARNING - Shape validation failed: input_shape: (1, 500), minimum_shape: (chains=2, draws=4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>15.207</td>\n",
       "      <td>5.127</td>\n",
       "      <td>6.969</td>\n",
       "      <td>26.097</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.159</td>\n",
       "      <td>552.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>14.941</td>\n",
       "      <td>10.196</td>\n",
       "      <td>2.099</td>\n",
       "      <td>33.381</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.394</td>\n",
       "      <td>467.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.592</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.007</td>\n",
       "      <td>391.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.417</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "      <td>458.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean      sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha  15.207   5.127   6.969   26.097      0.220    0.159     552.0   \n",
       "beta   14.941  10.196   2.099   33.381      0.503    0.394     467.0   \n",
       "r       0.592   0.209   0.253    0.973      0.010    0.007     391.0   \n",
       "s       0.417   0.192   0.143    0.786      0.009    0.007     458.0   \n",
       "\n",
       "       ess_tail  r_hat  \n",
       "alpha     428.0    NaN  \n",
       "beta      363.0    NaN  \n",
       "r         409.0    NaN  \n",
       "s         462.0    NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnbd_fullrank.fit_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(pnbd_advi.approx.hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axes = plt.subplots(\n",
    "    nrows=2, ncols=2, figsize=(12, 7), sharex=False, sharey=False, layout=\"constrained\"\n",
    ")\n",
    "\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, var_name in enumerate([\"r\", \"alpha\", \"s\", \"beta\"]):\n",
    "    ax = axes[i]\n",
    "    az.plot_posterior(\n",
    "        pnbd_full.idata.posterior[var_name].values.flatten(),\n",
    "        color=\"C0\",\n",
    "        point_estimate=\"mean\",\n",
    "        ax=ax,\n",
    "        label=\"DEMZ\",\n",
    "    )\n",
    "    az.plot_posterior(\n",
    "        pnbd_advi.idata.posterior[var_name].values.flatten(),\n",
    "        color=\"C1\",\n",
    "        point_estimate=\"mean\",\n",
    "        ax=ax,\n",
    "        label=\"ADVI\",\n",
    "    )\n",
    "    # az.plot_posterior(\n",
    "    #     pnbd_fullrank.idata.posterior[var_name].values.flatten(),\n",
    "    #     color=\"C2\",\n",
    "    #     point_estimate=\"mean\",\n",
    "    #     ax=ax,\n",
    "    #     label=\"FULLRANK_ADVI\",\n",
    "    # )\n",
    "    ax.axvline(x=map_fit[var_name], color=\"C3\", linestyle=\"--\", label=\"MAP\")\n",
    "    ax.legend(loc=\"upper right\")\n",
    "    ax.set_title(var_name)\n",
    "\n",
    "plt.gcf().suptitle(\"Pareto/NBD Model Parameters - DEMZ vs ADVI fits\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axes = plt.subplots(\n",
    "    nrows=2, ncols=2, figsize=(12, 7), sharex=False, sharey=False, layout=\"constrained\"\n",
    ")\n",
    "\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, var_name in enumerate([\"r\", \"alpha\", \"s\", \"beta\"]):\n",
    "    ax = axes[i]\n",
    "    az.plot_posterior(\n",
    "        pnbd_full.idata.posterior[var_name].values.flatten(),\n",
    "        color=\"C0\",\n",
    "        point_estimate=\"mean\",\n",
    "        ax=ax,\n",
    "        label=\"DEMZ\",\n",
    "    )\n",
    "    # az.plot_posterior(\n",
    "    #     pnbd_advi.idata.posterior[var_name].values.flatten(),\n",
    "    #     color=\"C1\",\n",
    "    #     point_estimate=\"mean\",\n",
    "    #     ax=ax,\n",
    "    #     label=\"ADVI\",\n",
    "    # )\n",
    "    az.plot_posterior(\n",
    "        pnbd_fullrank.idata.posterior[var_name].values.flatten(),\n",
    "        color=\"C2\",\n",
    "        point_estimate=\"mean\",\n",
    "        ax=ax,\n",
    "        label=\"FULLRANK_ADVI\",\n",
    "    )\n",
    "    ax.axvline(x=map_fit[var_name], color=\"C3\", linestyle=\"--\", label=\"MAP\")\n",
    "    ax.legend(loc=\"upper right\")\n",
    "    ax.set_title(var_name)\n",
    "\n",
    "plt.gcf().suptitle(\"Pareto/NBD Model Parameters - DEMZ vs FULLRANK fits\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "- Fullrank provides a rather poor fit\n",
    "- advi's fit does match mcmc, although providing narrower estimates\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relative Differences in param estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "arviz - WARNING - Shape validation failed: input_shape: (1, 500), minimum_shape: (chains=2, draws=4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pcnt_relative_diff_param_mean</th>\n",
       "      <th>pcnt_relative_diff_param_sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.429102</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>1.717647</td>\n",
       "      <td>68.572203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>1.130856</td>\n",
       "      <td>58.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.464037</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pcnt_relative_diff_param_mean  pcnt_relative_diff_param_sd\n",
       "alpha                       0.429102                    46.000000\n",
       "beta                        1.717647                    68.572203\n",
       "r                           1.130856                    58.695652\n",
       "s                           0.464037                    65.000000"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    100*(pnbd_full.fit_summary()[['mean', 'sd']] - pnbd_advi.fit_summary()[['mean', 'sd']]) / \n",
    "    pnbd_full.fit_summary()[['mean', 'sd']]\n",
    "    ).rename(\n",
    "    columns={\n",
    "        \"mean\": \"pcnt_relative_diff_param_mean\",\n",
    "        \"sd\": \"pcnt_relative_diff_param_sd\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pymc-marketing-dev",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}