From 0db42e596a71742fb02c09702f10383ce7696219 Mon Sep 17 00:00:00 2001
From: Ankur Ankan <ankurankan@gmail.com>
Date: Wed, 1 Nov 2023 08:18:36 +0100
Subject: [PATCH] Updates causal inference docs

---
 _modules/pgmpy/base/DAG.html                  |  25 +---
 .../pgmpy/estimators/BayesianEstimator.html   |  37 ++---
 _modules/pgmpy/estimators/CITests.html        |  25 +---
 _modules/pgmpy/estimators/EM.html             |  96 +++++++-----
 .../pgmpy/estimators/ExhaustiveSearch.html    |  31 +---
 .../pgmpy/estimators/HillClimbSearch.html     |  31 +---
 _modules/pgmpy/estimators/MLE.html            |  40 ++---
 _modules/pgmpy/estimators/MmhcEstimator.html  |  31 +---
 _modules/pgmpy/estimators/PC.html             |  25 +---
 _modules/pgmpy/estimators/SEMEstimator.html   |  25 +---
 _modules/pgmpy/estimators/StructureScore.html |  61 +-------
 _modules/pgmpy/estimators/TreeSearch.html     |  25 +---
 _modules/pgmpy/factors/discrete/CPD.html      |  33 ++--
 .../factors/discrete/DiscreteFactor.html      |  25 +---
 .../JointProbabilityDistribution.html         |  25 +---
 _modules/pgmpy/inference/ApproxInference.html |  25 +---
 _modules/pgmpy/inference/CausalInference.html | 141 +++++++-----------
 .../pgmpy/inference/EliminationOrder.html     |  25 +---
 _modules/pgmpy/inference/ExactInference.html  |  25 +---
 _modules/pgmpy/inference/dbn_inference.html   |  25 +---
 _modules/pgmpy/metrics/bn_inference.html      |  29 ++--
 _modules/pgmpy/metrics/metrics.html           |  31 ++--
 _modules/pgmpy/models/BayesianNetwork.html    |  50 ++-----
 .../pgmpy/models/DynamicBayesianNetwork.html  |  25 +---
 _modules/pgmpy/models/FactorGraph.html        |  25 +---
 _modules/pgmpy/models/MarkovChain.html        |  25 +---
 _modules/pgmpy/models/MarkovNetwork.html      |  25 +---
 _modules/pgmpy/models/NoisyOrModel.html       |  25 +---
 _modules/pgmpy/models/SEM.html                |  25 +---
 _modules/pgmpy/readwrite/BIF.html             |  25 +---
 _modules/pgmpy/readwrite/PomdpX.html          |  25 +---
 _modules/pgmpy/readwrite/UAI.html             |  25 +---
 _modules/pgmpy/readwrite/XMLBIF.html          |  25 +---
 .../pgmpy/readwrite/XMLBeliefNetwork.html     |  25 +---
 _modules/pgmpy/sampling/Sampling.html         |  29 ++--
 base.html                                     |   6 +-
 base/base.html                                |   6 +-
 exact_infer/causal.html                       | 122 ++++++---------
 exact_infer/model_testing.html                |   6 +-
 examples.html                                 |  18 +--
 factors/discrete.html                         |   2 +-
 genindex.html                                 |   6 -
 metrics/metrics.html                          |   6 +-
 models/bayesiannetwork.html                   |  19 +--
 models/dbn.html                               |   6 +-
 objects.inv                                   | Bin 25732 -> 15464 bytes
 param_estimator/bayesian_est.html             |   6 +-
 param_estimator/em.html                       |  10 +-
 param_estimator/mle.html                      |   8 +-
 searchindex.js                                |   2 +-
 structure_estimator/exhaustive.html           |   6 +-
 structure_estimator/hill.html                 |  22 +--
 structure_estimator/mmhc.html                 |   6 +-
 structure_estimator/pc.html                   |   4 +-
 structure_estimator/tree.html                 |   2 +-
 tutorial.html                                 |  19 +--
 56 files changed, 504 insertions(+), 968 deletions(-)

diff --git a/_modules/pgmpy/base/DAG.html b/_modules/pgmpy/base/DAG.html
index defc20d..e4e9788 100644
--- a/_modules/pgmpy/base/DAG.html
+++ b/_modules/pgmpy/base/DAG.html
@@ -1324,7 +1324,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -1351,21 +1358,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
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-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/estimators/BayesianEstimator.html b/_modules/pgmpy/estimators/BayesianEstimator.html
index bea795f..15b8a34 100644
--- a/_modules/pgmpy/estimators/BayesianEstimator.html
+++ b/_modules/pgmpy/estimators/BayesianEstimator.html
@@ -68,7 +68,7 @@ <h1>Source code for pgmpy.estimators.BayesianEstimator</h1><div class="highlight
         <span class="n">prior_type</span><span class="o">=</span><span class="s2">&quot;BDeu&quot;</span><span class="p">,</span>
         <span class="n">equivalent_sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
         <span class="n">pseudo_counts</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
         <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
     <span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -92,6 +92,16 @@ <h1>Source code for pgmpy.estimators.BayesianEstimator</h1><div class="highlight
 <span class="sd">            - A prior_type of &#39;K2&#39; is a shorthand for &#39;dirichlet&#39; + setting every pseudo_count to 1,</span>
 <span class="sd">                regardless of the cardinality of the variable.</span>
 
+<span class="sd">        equivalent_sample_size: int</span>
+<span class="sd">            Refer `priort_type` for more details.</span>
+
+<span class="sd">        pseudo_counts: int (default: None)</span>
+<span class="sd">            Refer `priort_type` for more details.</span>
+
+<span class="sd">        n_jobs: int (default: 1)</span>
+<span class="sd">            Number of jobs to run in parallel. Default: 1 uses all the processors.</span>
+<span class="sd">            Using n_jobs &gt; 1 for small models might be slower.</span>
+
 <span class="sd">        weighted: bool</span>
 <span class="sd">            If weighted=True, the data must contain a `_weight` column specifying the</span>
 <span class="sd">            weight of each datapoint (row). If False, assigns an equal weight to each</span>
@@ -320,7 +330,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -347,21 +364,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', '405910108']);
-      _gaq.push(['_setDomainName', 'none']);
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-      _gaq.push(['_trackPageview']);
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-        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/estimators/CITests.html b/_modules/pgmpy/estimators/CITests.html
index 69d934f..a6d8e9a 100644
--- a/_modules/pgmpy/estimators/CITests.html
+++ b/_modules/pgmpy/estimators/CITests.html
@@ -733,7 +733,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -760,21 +767,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
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-      })();
-
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-    
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 </html>
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diff --git a/_modules/pgmpy/estimators/EM.html b/_modules/pgmpy/estimators/EM.html
index 2ced999..4c7fc6d 100644
--- a/_modules/pgmpy/estimators/EM.html
+++ b/_modules/pgmpy/estimators/EM.html
@@ -70,12 +70,6 @@ <h1>Source code for pgmpy.estimators.EM</h1><div class="highlight"><pre>
 <span class="sd">        that the variable can take. If unspecified, the observed values in</span>
 <span class="sd">        the data set are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to</span>
-<span class="sd">        `True` all rows that contain `np.NaN` somewhere are ignored. If</span>
-<span class="sd">        `False` then, for each variable, every row where neither the</span>
-<span class="sd">        variable nor its parents are `np.NaN` is used.</span>
-
 <span class="sd">    Examples</span>
 <span class="sd">    --------</span>
 <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
@@ -107,8 +101,15 @@ <h1>Source code for pgmpy.estimators.EM</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">cpd</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span><span class="o">.</span><span class="n">cpds</span><span class="p">:</span>
             <span class="n">scope</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">cpd</span><span class="o">.</span><span class="n">scope</span><span class="p">())</span>
             <span class="n">likelihood</span> <span class="o">+=</span> <span class="n">log</span><span class="p">(</span>
-                <span class="n">cpd</span><span class="o">.</span><span class="n">get_value</span><span class="p">(</span>
-                    <span class="o">**</span><span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">value</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">datapoint</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">scope</span><span class="p">}</span>
+                <span class="nb">max</span><span class="p">(</span>
+                    <span class="n">cpd</span><span class="o">.</span><span class="n">get_value</span><span class="p">(</span>
+                        <span class="o">**</span><span class="p">{</span>
+                            <span class="n">key</span><span class="p">:</span> <span class="n">value</span>
+                            <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">datapoint</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+                            <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">scope</span>
+                        <span class="p">}</span>
+                    <span class="p">),</span>
+                    <span class="mf">1e-10</span><span class="p">,</span>
                 <span class="p">)</span>
             <span class="p">)</span>
         <span class="k">return</span> <span class="n">likelihood</span>
@@ -126,7 +127,9 @@ <h1>Source code for pgmpy.estimators.EM</h1><div class="highlight"><pre>
             <span class="p">)</span>
             <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">latent_var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">latent_card</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
                 <span class="n">df</span><span class="p">[</span><span class="n">latent_var</span><span class="p">]</span> <span class="o">=</span> <span class="n">latent_combinations</span><span class="p">[:,</span> <span class="n">index</span><span class="p">]</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_log_likelihood</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">t</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">e</span> <span class="o">**</span> <span class="p">(</span>
+                <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_log_likelihood</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">t</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="p">)</span>
             <span class="n">df</span><span class="p">[</span><span class="s2">&quot;_weight&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">weights</span> <span class="o">/</span> <span class="n">weights</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> <span class="o">*</span> <span class="n">n_counts</span><span class="p">[</span>
                 <span class="nb">tuple</span><span class="p">(</span><span class="n">data_unique</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
             <span class="p">]</span>
@@ -167,7 +170,7 @@ <h1>Source code for pgmpy.estimators.EM</h1><div class="highlight"><pre>
         <span class="n">latent_card</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
         <span class="n">max_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
         <span class="n">atol</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">,</span>
-        <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
         <span class="n">batch_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
         <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
         <span class="n">show_progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
@@ -190,9 +193,9 @@ <h1>Source code for pgmpy.estimators.EM</h1><div class="highlight"><pre>
 <span class="sd">            The absolute accepted tolerance for checking convergence. If the parameters</span>
 <span class="sd">            change is less than atol in an iteration, the algorithm will exit.</span>
 
-<span class="sd">        n_jobs: int (default: -1)</span>
-<span class="sd">            Number of jobs to run in parallel. Default: -1 uses all the processors.</span>
-<span class="sd">            Suggest to use n_jobs=1 when dataset size is less than 1000.</span>
+<span class="sd">        n_jobs: int (default: 1)</span>
+<span class="sd">            Number of jobs to run in parallel.</span>
+<span class="sd">            Using n_jobs &gt; 1 for small models or datasets might be slower.</span>
 
 <span class="sd">        batch_size: int (default: 1000)</span>
 <span class="sd">            Number of data used to compute weights in a batch.</span>
@@ -234,14 +237,30 @@ <h1>Source code for pgmpy.estimators.EM</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span><span class="o">.</span><span class="n">latents</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">state_names</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_states_dict</span><span class="p">[</span><span class="n">var</span><span class="p">]))</span>
 
-        <span class="c1"># Step 3: Initialize random CPDs if starting values aren&#39;t provided.</span>
-        <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+        <span class="c1"># Step 3: Initialize CPDs.</span>
+        <span class="c1"># Step 3.1: Learn the CPDs of variables which don&#39;t involve</span>
+        <span class="c1">#           latent variables using MLE.</span>
+        <span class="n">fixed_cpds</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">fixed_cpd_vars</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">nodes</span><span class="p">())</span>
+            <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span>
+            <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">chain</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">get_children</span><span class="p">(</span><span class="n">var</span><span class="p">)</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span><span class="p">]))</span>
+        <span class="p">)</span>
+
+        <span class="n">mle</span> <span class="o">=</span> <span class="n">MaximumLikelihoodEstimator</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">MaximumLikelihoodEstimator</span><span class="p">)</span>
+        <span class="n">mle</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span>
+        <span class="n">mle</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span>
+        <span class="n">mle</span><span class="o">.</span><span class="n">state_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_names</span>
+
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">fixed_cpd_vars</span><span class="p">:</span>
+            <span class="n">fixed_cpds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mle</span><span class="o">.</span><span class="n">estimate_cpd</span><span class="p">(</span><span class="n">var</span><span class="p">))</span>
 
-        <span class="n">cpds</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span><span class="o">.</span><span class="n">nodes</span><span class="p">():</span>
+        <span class="c1"># Step 3.2: Randomly initialize the CPDs involving latent variables.</span>
+        <span class="n">latent_cpds</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">vars_with_latents</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span><span class="o">.</span><span class="n">nodes</span><span class="p">())</span> <span class="o">-</span> <span class="n">fixed_cpd_vars</span>
+        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">vars_with_latents</span><span class="p">:</span>
             <span class="n">parents</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span><span class="o">.</span><span class="n">predecessors</span><span class="p">(</span><span class="n">node</span><span class="p">))</span>
-            <span class="n">cpds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <span class="n">latent_cpds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
                 <span class="n">TabularCPD</span><span class="o">.</span><span class="n">get_random</span><span class="p">(</span>
                     <span class="n">variable</span><span class="o">=</span><span class="n">node</span><span class="p">,</span>
                     <span class="n">evidence</span><span class="o">=</span><span class="n">parents</span><span class="p">,</span>
@@ -251,23 +270,27 @@ <h1>Source code for pgmpy.estimators.EM</h1><div class="highlight"><pre>
                     <span class="n">state_names</span><span class="o">=</span><span class="p">{</span>
                         <span class="n">var</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_names</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">chain</span><span class="p">([</span><span class="n">node</span><span class="p">],</span> <span class="n">parents</span><span class="p">)</span>
                     <span class="p">},</span>
+                    <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
                 <span class="p">)</span>
             <span class="p">)</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span><span class="o">.</span><span class="n">add_cpds</span><span class="p">(</span><span class="o">*</span><span class="n">cpds</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span><span class="o">.</span><span class="n">add_cpds</span><span class="p">(</span><span class="o">*</span><span class="nb">list</span><span class="p">(</span><span class="n">chain</span><span class="p">(</span><span class="n">fixed_cpds</span><span class="p">,</span> <span class="n">latent_cpds</span><span class="p">)))</span>
 
         <span class="k">if</span> <span class="n">show_progress</span> <span class="ow">and</span> <span class="n">config</span><span class="o">.</span><span class="n">SHOW_PROGRESS</span><span class="p">:</span>
             <span class="n">pbar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="n">max_iter</span><span class="p">)</span>
 
+        <span class="n">mle</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span>
         <span class="c1"># Step 4: Run the EM algorithm.</span>
         <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iter</span><span class="p">):</span>
             <span class="c1"># Step 4.1: E-step: Expands the dataset and computes the likelihood of each</span>
             <span class="c1">#           possible state of latent variables.</span>
             <span class="n">weighted_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_weights</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">latent_card</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
             <span class="c1"># Step 4.2: M-step: Uses the weights of the dataset to do a weighted MLE.</span>
-            <span class="n">new_cpds</span> <span class="o">=</span> <span class="n">MaximumLikelihoodEstimator</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">model_copy</span><span class="p">,</span> <span class="n">weighted_data</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">get_parameters</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">weighted</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">new_cpds</span> <span class="o">=</span> <span class="n">fixed_cpds</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">mle</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">weighted_data</span>
+            <span class="c1"># mle = MaximumLikelihoodEstimator(self.model_copy, weighted_data)</span>
+            <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">vars_with_latents</span><span class="p">:</span>
+                <span class="n">new_cpds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mle</span><span class="o">.</span><span class="n">estimate_cpd</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">weighted</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
 
             <span class="c1"># Step 4.3: Check of convergence and max_iter</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_converged</span><span class="p">(</span><span class="n">new_cpds</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">):</span>
@@ -280,7 +303,7 @@ <h1>Source code for pgmpy.estimators.EM</h1><div class="highlight"><pre>
                 <span class="k">if</span> <span class="n">show_progress</span> <span class="ow">and</span> <span class="n">config</span><span class="o">.</span><span class="n">SHOW_PROGRESS</span><span class="p">:</span>
                     <span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">cpds</span></div></div>
+        <span class="k">return</span> <span class="n">new_cpds</span></div></div>
 </pre></div>
 
           </div>
@@ -338,7 +361,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -365,21 +395,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
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-      _gaq.push(['_setAccount', '405910108']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
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diff --git a/_modules/pgmpy/estimators/ExhaustiveSearch.html b/_modules/pgmpy/estimators/ExhaustiveSearch.html
index e7ee884..577cfbb 100644
--- a/_modules/pgmpy/estimators/ExhaustiveSearch.html
+++ b/_modules/pgmpy/estimators/ExhaustiveSearch.html
@@ -68,12 +68,6 @@ <h1>Source code for pgmpy.estimators.ExhaustiveSearch</h1><div class="highlight"
 <span class="sd">        If True, uses caching of score for faster computation.</span>
 <span class="sd">        Note: Caching only works for scoring methods which are decomposable. Can</span>
 <span class="sd">        give wrong results in case of custom scoring methods.</span>
-
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">scoring_method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_cache</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
@@ -286,7 +280,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -313,21 +314,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', '405910108']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
-      (function() {
-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
-        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/estimators/HillClimbSearch.html b/_modules/pgmpy/estimators/HillClimbSearch.html
index fa3c786..8d3288b 100644
--- a/_modules/pgmpy/estimators/HillClimbSearch.html
+++ b/_modules/pgmpy/estimators/HillClimbSearch.html
@@ -69,12 +69,6 @@ <h1>Source code for pgmpy.estimators.HillClimbSearch</h1><div class="highlight">
 <span class="sd">        that the variable can take. If unspecified, the observed values in the data set</span>
 <span class="sd">        are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
-
 <span class="sd">    use_caching: boolean</span>
 <span class="sd">        If True, uses caching of score for faster computation.</span>
 <span class="sd">        Note: Caching only works for scoring methods which are decomposable. Can</span>
@@ -424,7 +418,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -451,21 +452,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', '405910108']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
-      (function() {
-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
-        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/estimators/MLE.html b/_modules/pgmpy/estimators/MLE.html
index 980d926..0eb6347 100644
--- a/_modules/pgmpy/estimators/MLE.html
+++ b/_modules/pgmpy/estimators/MLE.html
@@ -61,11 +61,6 @@ <h1>Source code for pgmpy.estimators.MLE</h1><div class="highlight"><pre>
 <span class="sd">        that the variable can take. If unspecified, the observed values</span>
 <span class="sd">        in the data set are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.NaN` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-
 <span class="sd">    Examples</span>
 <span class="sd">    --------</span>
 <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
@@ -83,22 +78,24 @@ <h1>Source code for pgmpy.estimators.MLE</h1><div class="highlight"><pre>
             <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
                 <span class="s2">&quot;Maximum Likelihood Estimate is only implemented for BayesianNetwork&quot;</span>
             <span class="p">)</span>
+
         <span class="k">elif</span> <span class="nb">set</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">nodes</span><span class="p">())</span> <span class="o">&gt;</span> <span class="nb">set</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;Maximum Likelihood Estimator works only for models with all observed variables. Found latent variables: </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span><span class="si">}</span><span class="s2">.&quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Found latent variables: </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span><span class="si">}</span><span class="s2">. Maximum Likelihood doesn&#39;t support latent variables, please use ExpectationMaximization&quot;</span>
             <span class="p">)</span>
 
         <span class="nb">super</span><span class="p">(</span><span class="n">MaximumLikelihoodEstimator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
-<div class="viewcode-block" id="MaximumLikelihoodEstimator.get_parameters"><a class="viewcode-back" href="../../../param_estimator/mle.html#pgmpy.estimators.MLE.MaximumLikelihoodEstimator.get_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">get_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="MaximumLikelihoodEstimator.get_parameters"><a class="viewcode-back" href="../../../param_estimator/mle.html#pgmpy.estimators.MLE.MaximumLikelihoodEstimator.get_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">get_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">weighted</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Method to estimate the model parameters (CPDs) using Maximum Likelihood</span>
 <span class="sd">        Estimation.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        n_jobs: int (default: -1)</span>
-<span class="sd">            Number of jobs to run in parallel. Default: -1 uses all the processors.</span>
+<span class="sd">        n_jobs: int (default: 1)</span>
+<span class="sd">            Number of jobs to run in parallel. Default: 1 uses all the processors.</span>
+<span class="sd">            Using n_jobs &gt; 1 for small models might be slower.</span>
 
 <span class="sd">        weighted: bool</span>
 <span class="sd">            If weighted=True, the data must contain a `_weight` column specifying the</span>
@@ -268,7 +265,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -295,21 +299,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', '405910108']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
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-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
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-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
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   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/estimators/MmhcEstimator.html b/_modules/pgmpy/estimators/MmhcEstimator.html
index bf78114..ac35823 100644
--- a/_modules/pgmpy/estimators/MmhcEstimator.html
+++ b/_modules/pgmpy/estimators/MmhcEstimator.html
@@ -57,12 +57,6 @@ <h1>Source code for pgmpy.estimators.MmhcEstimator</h1><div class="highlight"><p
 <span class="sd">        that the variable can take. If unspecified, the observed values in the data set</span>
 <span class="sd">        are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
-
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
 <span class="sd">    Tsamardinos et al., The max-min hill-climbing Bayesian network structure learning algorithm (2005)</span>
@@ -311,7 +305,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
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+</script>
 <div id="searchbox" style="display: none" role="search">
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@@ -338,21 +339,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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diff --git a/_modules/pgmpy/estimators/PC.html b/_modules/pgmpy/estimators/PC.html
index 3832ede..aa74301 100644
--- a/_modules/pgmpy/estimators/PC.html
+++ b/_modules/pgmpy/estimators/PC.html
@@ -568,7 +568,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
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+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -595,21 +602,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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diff --git a/_modules/pgmpy/estimators/SEMEstimator.html b/_modules/pgmpy/estimators/SEMEstimator.html
index 8d39cd1..a723c7f 100644
--- a/_modules/pgmpy/estimators/SEMEstimator.html
+++ b/_modules/pgmpy/estimators/SEMEstimator.html
@@ -539,7 +539,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
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   <h3 id="searchlabel">Quick search</h3>
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@@ -566,21 +573,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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diff --git a/_modules/pgmpy/estimators/StructureScore.html b/_modules/pgmpy/estimators/StructureScore.html
index 7066d43..4ebf96a 100644
--- a/_modules/pgmpy/estimators/StructureScore.html
+++ b/_modules/pgmpy/estimators/StructureScore.html
@@ -58,12 +58,6 @@ <h1>Source code for pgmpy.estimators.StructureScore</h1><div class="highlight"><
 <span class="sd">        that the variable can take. If unspecified, the observed values in the data set</span>
 <span class="sd">        are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
-
 <span class="sd">    Reference</span>
 <span class="sd">    ---------</span>
 <span class="sd">    Koller &amp; Friedman, Probabilistic Graphical Models - Principles and Techniques, 2009</span>
@@ -139,12 +133,6 @@ <h1>Source code for pgmpy.estimators.StructureScore</h1><div class="highlight"><
 <span class="sd">        that the variable can take. If unspecified, the observed values in the data set</span>
 <span class="sd">        are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
-
 <span class="sd">    References</span>
 <span class="sd">    ---------</span>
 <span class="sd">    [1] Koller &amp; Friedman, Probabilistic Graphical Models - Principles and Techniques, 2009</span>
@@ -216,12 +204,6 @@ <h1>Source code for pgmpy.estimators.StructureScore</h1><div class="highlight"><
 <span class="sd">        that the variable can take. If unspecified, the observed values in the data set</span>
 <span class="sd">        are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
-
 <span class="sd">    References</span>
 <span class="sd">    ---------</span>
 <span class="sd">    [1] Koller &amp; Friedman, Probabilistic Graphical Models - Principles and Techniques, 2009</span>
@@ -302,12 +284,6 @@ <h1>Source code for pgmpy.estimators.StructureScore</h1><div class="highlight"><
 <span class="sd">        that the variable can take. If unspecified, the observed values in the data set</span>
 <span class="sd">        are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
-
 <span class="sd">    References</span>
 <span class="sd">    ---------</span>
 <span class="sd">    [1] Scutari, Marco. An Empirical-Bayes Score for Discrete Bayesian Networks.</span>
@@ -400,12 +376,6 @@ <h1>Source code for pgmpy.estimators.StructureScore</h1><div class="highlight"><
 <span class="sd">        that the variable can take. If unspecified, the observed values in the data set</span>
 <span class="sd">        are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
-
 <span class="sd">    References</span>
 <span class="sd">    ---------</span>
 <span class="sd">    [1] Koller &amp; Friedman, Probabilistic Graphical Models - Principles and Techniques, 2009</span>
@@ -468,12 +438,6 @@ <h1>Source code for pgmpy.estimators.StructureScore</h1><div class="highlight"><
 <span class="sd">        that the variable can take. If unspecified, the observed values in the data set</span>
 <span class="sd">        are taken to be the only possible states.</span>
 
-<span class="sd">    complete_samples_only: bool (optional, default `True`)</span>
-<span class="sd">        Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">        that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">        every row where neither the variable nor its parents are `np.NaN` is used.</span>
-<span class="sd">        This sets the behavior of the `state_count`-method.</span>
-
 <span class="sd">    References</span>
 <span class="sd">    ---------</span>
 <span class="sd">    [1] Koller &amp; Friedman, Probabilistic Graphical Models - Principles and Techniques, 2009</span>
@@ -571,7 +535,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -598,21 +569,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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   </body>
 </html>
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diff --git a/_modules/pgmpy/estimators/TreeSearch.html b/_modules/pgmpy/estimators/TreeSearch.html
index f8ccd23..2e33e55 100644
--- a/_modules/pgmpy/estimators/TreeSearch.html
+++ b/_modules/pgmpy/estimators/TreeSearch.html
@@ -478,7 +478,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -505,21 +512,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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diff --git a/_modules/pgmpy/factors/discrete/CPD.html b/_modules/pgmpy/factors/discrete/CPD.html
index 5bedae9..f1b7110 100644
--- a/_modules/pgmpy/factors/discrete/CPD.html
+++ b/_modules/pgmpy/factors/discrete/CPD.html
@@ -630,7 +630,7 @@ <h1>Source code for pgmpy.factors.discrete.CPD</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">[:</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span></div>
 
 <div class="viewcode-block" id="TabularCPD.get_random"><a class="viewcode-back" href="../../../../factors/discrete.html#pgmpy.factors.discrete.CPD.TabularCPD.get_random">[docs]</a>    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">get_random</span><span class="p">(</span><span class="n">variable</span><span class="p">,</span> <span class="n">evidence</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cardinality</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">state_names</span><span class="o">=</span><span class="p">{}):</span>
+    <span class="k">def</span> <span class="nf">get_random</span><span class="p">(</span><span class="n">variable</span><span class="p">,</span> <span class="n">evidence</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cardinality</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">state_names</span><span class="o">=</span><span class="p">{},</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Generates a TabularCPD instance with random values on `variable` with</span>
 <span class="sd">        parents/evidence `evidence` with cardinality/number of states as given</span>
@@ -671,6 +671,8 @@ <h1>Source code for pgmpy.factors.discrete.CPD</h1><div class="highlight"><pre>
 <span class="sd">        ...                         &#39;B&#39;: [&#39;b1&#39;, &#39;b2&#39;],</span>
 <span class="sd">        ...                         &#39;C&#39;: [&#39;c1&#39;, &#39;c2&#39;]})</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">generator</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+
         <span class="k">if</span> <span class="n">evidence</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">evidence</span> <span class="o">=</span> <span class="p">[]</span>
 
@@ -682,7 +684,7 @@ <h1>Source code for pgmpy.factors.discrete.CPD</h1><div class="highlight"><pre>
                     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Cardinality for variable: </span><span class="si">{</span><span class="n">var</span><span class="si">}</span><span class="s2"> not specified.&quot;</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">evidence</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">cardinality</span><span class="p">[</span><span class="n">variable</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">values</span> <span class="o">=</span> <span class="n">generator</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">cardinality</span><span class="p">[</span><span class="n">variable</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
             <span class="n">values</span> <span class="o">=</span> <span class="n">values</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
             <span class="n">node_cpd</span> <span class="o">=</span> <span class="n">TabularCPD</span><span class="p">(</span>
                 <span class="n">variable</span><span class="o">=</span><span class="n">variable</span><span class="p">,</span>
@@ -692,7 +694,7 @@ <h1>Source code for pgmpy.factors.discrete.CPD</h1><div class="highlight"><pre>
             <span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">parent_card</span> <span class="o">=</span> <span class="p">[</span><span class="n">cardinality</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">evidence</span><span class="p">]</span>
-            <span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">cardinality</span><span class="p">[</span><span class="n">variable</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">parent_card</span><span class="p">))</span>
+            <span class="n">values</span> <span class="o">=</span> <span class="n">generator</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">cardinality</span><span class="p">[</span><span class="n">variable</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">parent_card</span><span class="p">)))</span>
             <span class="n">values</span> <span class="o">=</span> <span class="n">values</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
             <span class="n">node_cpd</span> <span class="o">=</span> <span class="n">TabularCPD</span><span class="p">(</span>
                 <span class="n">variable</span><span class="o">=</span><span class="n">variable</span><span class="p">,</span>
@@ -761,7 +763,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -788,21 +797,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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   </body>
 </html>
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diff --git a/_modules/pgmpy/factors/discrete/DiscreteFactor.html b/_modules/pgmpy/factors/discrete/DiscreteFactor.html
index e9ccd8e..6f1d4ff 100644
--- a/_modules/pgmpy/factors/discrete/DiscreteFactor.html
+++ b/_modules/pgmpy/factors/discrete/DiscreteFactor.html
@@ -1126,7 +1126,14 @@ <h3>Related Topics</h3>
 
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diff --git a/_modules/pgmpy/factors/discrete/JointProbabilityDistribution.html b/_modules/pgmpy/factors/discrete/JointProbabilityDistribution.html
index f2209be..d4e82f8 100644
--- a/_modules/pgmpy/factors/discrete/JointProbabilityDistribution.html
+++ b/_modules/pgmpy/factors/discrete/JointProbabilityDistribution.html
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diff --git a/_modules/pgmpy/inference/ApproxInference.html b/_modules/pgmpy/inference/ApproxInference.html
index 0b8193d..e9d89a1 100644
--- a/_modules/pgmpy/inference/ApproxInference.html
+++ b/_modules/pgmpy/inference/ApproxInference.html
@@ -286,7 +286,14 @@ <h3>Related Topics</h3>
 
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diff --git a/_modules/pgmpy/inference/CausalInference.html b/_modules/pgmpy/inference/CausalInference.html
index 1a9be0e..679b9e2 100644
--- a/_modules/pgmpy/inference/CausalInference.html
+++ b/_modules/pgmpy/inference/CausalInference.html
@@ -47,32 +47,29 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 
 <div class="viewcode-block" id="CausalInference"><a class="viewcode-back" href="../../../exact_infer/causal.html#pgmpy.inference.CausalInference.CausalInference">[docs]</a><span class="k">class</span> <span class="nc">CausalInference</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This is an inference class for performing Causal Inference over Bayesian Networks or Structural Equation Models.</span>
-
-<span class="sd">    This class will accept queries of the form: P(Y | do(X)) and utilize its methods to provide an estimand which:</span>
-<span class="sd">     * Identifies adjustment variables</span>
-<span class="sd">     * Backdoor Adjustment</span>
-<span class="sd">     * Front Door Adjustment</span>
-<span class="sd">     * Instrumental Variable Adjustment</span>
+<span class="sd">    This is an inference class for performing Causal Inference over Bayesian</span>
+<span class="sd">    Networks or Structural Equation Models.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
-<span class="sd">    model: CausalGraph</span>
+<span class="sd">    model: pgmpy.base.DAG | pgmpy.models.BayesianNetwork</span>
 <span class="sd">        The model that we&#39;ll perform inference over.</span>
 
 <span class="sd">    set_nodes: list[node:str] or None</span>
-<span class="sd">        A list (or set/tuple) of nodes in the Bayesian Network which have been set to a specific value per the</span>
-<span class="sd">        do-operator.</span>
+<span class="sd">        A list (or set/tuple) of nodes in the Bayesian Network which have been</span>
+<span class="sd">        set to a specific value per the do-operator.</span>
 
 <span class="sd">    Examples</span>
 <span class="sd">    --------</span>
 <span class="sd">    Create a small Bayesian Network.</span>
+
 <span class="sd">    &gt;&gt;&gt; from pgmpy.models import BayesianNetwork</span>
 <span class="sd">    &gt;&gt;&gt; game = BayesianNetwork([(&#39;X&#39;, &#39;A&#39;),</span>
 <span class="sd">    ...                         (&#39;A&#39;, &#39;Y&#39;),</span>
 <span class="sd">    ...                         (&#39;A&#39;, &#39;B&#39;)])</span>
 
 <span class="sd">    Load the graph into the CausalInference object to make causal queries.</span>
+
 <span class="sd">    &gt;&gt;&gt; from pgmpy.inference.CausalInference import CausalInference</span>
 <span class="sd">    &gt;&gt;&gt; inference = CausalInference(game)</span>
 <span class="sd">    &gt;&gt;&gt; inference.get_all_backdoor_adjustment_sets(X=&quot;X&quot;, Y=&quot;Y&quot;)</span>
@@ -81,9 +78,6 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
 <span class="sd">    &#39;Causality: Models, Reasoning, and Inference&#39; - Judea Pearl (2000)</span>
-
-<span class="sd">    Many thanks to @ijmbarr for their implementation of Causal Graphical models available. It served as an invaluable</span>
-<span class="sd">    reference. Available on GitHub: https://github.com/ijmbarr/causalgraphicalmodels</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">set_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -107,14 +101,15 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        X: str</span>
-<span class="sd">            Intervention Variable</span>
 
-<span class="sd">        Y: str</span>
-<span class="sd">            Target Variable</span>
+<span class="sd">        X: str (variable name)</span>
+<span class="sd">            The cause/exposure variables.</span>
+
+<span class="sd">        Y: str (variable name)</span>
+<span class="sd">            The outcome variable.</span>
 
-<span class="sd">        Z: str or set[str]</span>
-<span class="sd">            Adjustment variables</span>
+<span class="sd">        Z: list (array-like)</span>
+<span class="sd">            List of adjustment variables.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -146,19 +141,13 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 <span class="sd">            (i) no node in Z is a descendant of Xi; and</span>
 <span class="sd">            (ii) Z blocks every path between Xi and Xj that contains an arrow into Xi.</span>
 
-<span class="sd">        TODO:</span>
-<span class="sd">          * Backdoors are great, but the most general things we could implement would be Ilya Shpitser&#39;s ID and</span>
-<span class="sd">            IDC algorithms. See [his Ph.D. thesis for a full explanation]</span>
-<span class="sd">            (https://ftp.cs.ucla.edu/pub/stat_ser/shpitser-thesis.pdf). After doing a little reading it is clear</span>
-<span class="sd">            that we do not need to immediatly implement this.  However, in order for us to truly account for</span>
-<span class="sd">            unobserved variables, we will need not only these algorithms, but a more general implementation of a DAG.</span>
-<span class="sd">            Most DAGs do not allow for bidirected edges, but it is an important piece of notation which Pearl and</span>
-<span class="sd">            Shpitser use to denote graphs with latent variables.</span>
-
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        X: str</span>
-<span class="sd">            Intervention Variable</span>
+<span class="sd">        X: str (variable name)</span>
+<span class="sd">            The cause/exposure variables.</span>
+
+<span class="sd">        Y: str (variable name)</span>
+<span class="sd">            The outcome variable.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -175,10 +164,6 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 <span class="sd">        &gt;&gt;&gt; inference = CausalInference(game1)</span>
 <span class="sd">        &gt;&gt;&gt; inference.get_all_backdoor_adjustment_sets(&quot;X&quot;, &quot;Y&quot;)</span>
 <span class="sd">        frozenset()</span>
-
-<span class="sd">        References</span>
-<span class="sd">        ----------</span>
-<span class="sd">        &quot;Causality: Models, Reasoning, and Inference&quot;, Judea Pearl (2000). p.79.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="k">assert</span> <span class="n">X</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_variables</span>
@@ -218,14 +203,14 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        X: str</span>
-<span class="sd">            Intervention Variable</span>
+<span class="sd">        X: str (variable name)</span>
+<span class="sd">            The cause/exposure variables.</span>
 
-<span class="sd">        Y: str</span>
-<span class="sd">            Target Variable</span>
+<span class="sd">        Y: str (variable name)</span>
+<span class="sd">            The outcome variable.</span>
 
-<span class="sd">        Z: set</span>
-<span class="sd">            Adjustment variables</span>
+<span class="sd">        Z: list (array-like)</span>
+<span class="sd">            List of adjustment variables.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -274,13 +259,17 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 <span class="sd">          (ii)   there is no backdoor path from X to Z</span>
 <span class="sd">          (iii)  all back-door paths from Z to Y are blocked by X</span>
 
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X: str (variable name)</span>
+<span class="sd">            The cause/exposure variables.</span>
+
+<span class="sd">        Y: str (variable name)</span>
+<span class="sd">            The outcome variable</span>
+
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        frozenset: a frozenset of frozensets</span>
-
-<span class="sd">        References</span>
-<span class="sd">        ----------</span>
-<span class="sd">        Causality: Models, Reasoning, and Inference, Judea Pearl (2000). p.82.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="n">X</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_variables</span>
         <span class="k">assert</span> <span class="n">Y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">observed_variables</span>
@@ -297,27 +286,7 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 
         <span class="k">return</span> <span class="n">valid_adjustment_sets</span></div>
 
-<div class="viewcode-block" id="CausalInference.get_distribution"><a class="viewcode-back" href="../../../exact_infer/causal.html#pgmpy.inference.CausalInference.CausalInference.get_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">get_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns a string representing the factorized distribution implied by the CGM.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">products</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">nx</span><span class="o">.</span><span class="n">topological_sort</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">set_nodes</span><span class="p">:</span>
-                <span class="k">continue</span>
-
-            <span class="n">parents</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predecessors</span><span class="p">(</span><span class="n">node</span><span class="p">))</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">parents</span><span class="p">:</span>
-                <span class="n">p</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;P(</span><span class="si">{</span><span class="n">node</span><span class="si">}</span><span class="s2">)&quot;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">parents</span> <span class="o">=</span> <span class="p">[</span>
-                    <span class="sa">f</span><span class="s2">&quot;do(</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">)&quot;</span> <span class="k">if</span> <span class="n">n</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">set_nodes</span> <span class="k">else</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">parents</span>
-                <span class="p">]</span>
-                <span class="n">p</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;P(</span><span class="si">{</span><span class="n">node</span><span class="si">}</span><span class="s2">|</span><span class="si">{</span><span class="s1">&#39;,&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">parents</span><span class="p">)</span><span class="si">}</span><span class="s2">)&quot;</span>
-            <span class="n">products</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="k">return</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">products</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="CausalInference.simple_decision"><a class="viewcode-back" href="../../../exact_infer/causal.html#pgmpy.inference.CausalInference.CausalInference.simple_decision">[docs]</a>    <span class="k">def</span> <span class="nf">simple_decision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">adjustment_sets</span><span class="o">=</span><span class="p">[]):</span>
+    <span class="k">def</span> <span class="nf">_simple_decision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">adjustment_sets</span><span class="o">=</span><span class="p">[]):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Selects the smallest set from provided adjustment sets.</span>
 
@@ -333,7 +302,7 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
         <span class="n">adjustment_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">adjustment_sets</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">adjustment_list</span> <span class="o">==</span> <span class="p">[]:</span>
             <span class="k">return</span> <span class="nb">frozenset</span><span class="p">([])</span>
-        <span class="k">return</span> <span class="n">adjustment_list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">adjustment_list</span><span class="p">)]</span></div>
+        <span class="k">return</span> <span class="n">adjustment_list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">adjustment_list</span><span class="p">)]</span>
 
 <div class="viewcode-block" id="CausalInference.estimate_ate"><a class="viewcode-back" href="../../../exact_infer/causal.html#pgmpy.inference.CausalInference.CausalInference.estimate_ate">[docs]</a>    <span class="k">def</span> <span class="nf">estimate_ate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
@@ -349,11 +318,11 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        X: str</span>
-<span class="sd">            Intervention Variable</span>
+<span class="sd">        X: str (variable name)</span>
+<span class="sd">            The cause/exposure variables.</span>
 
-<span class="sd">        Y: str</span>
-<span class="sd">            Target Variable</span>
+<span class="sd">        Y: str (variable name)</span>
+<span class="sd">            The outcome variable</span>
 
 <span class="sd">        data: pandas.DataFrame</span>
 <span class="sd">            All observed data for this Bayesian Network.</span>
@@ -415,7 +384,7 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
                     <span class="n">adjustment_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_all_backdoor_adjustment_sets</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
                     <span class="k">if</span> <span class="n">estimand_strategy</span> <span class="o">==</span> <span class="s2">&quot;smallest&quot;</span><span class="p">:</span>
                         <span class="n">adjustment_sets</span> <span class="o">=</span> <span class="nb">frozenset</span><span class="p">(</span>
-                            <span class="p">{</span><span class="bp">self</span><span class="o">.</span><span class="n">simple_decision</span><span class="p">(</span><span class="n">adjustment_sets</span><span class="p">)}</span>
+                            <span class="p">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_simple_decision</span><span class="p">(</span><span class="n">adjustment_sets</span><span class="p">)}</span>
                         <span class="p">)</span>
 
                 <span class="k">if</span> <span class="n">estimator_type</span> <span class="o">==</span> <span class="s2">&quot;linear&quot;</span><span class="p">:</span>
@@ -538,6 +507,11 @@ <h1>Source code for pgmpy.inference.CausalInference</h1><div class="highlight"><
 
 <span class="sd">        Examples</span>
 <span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from pgmpy.models import BayesianNetwork</span>
+<span class="sd">        &gt;&gt;&gt; from pgmpy.inference import CausalInference</span>
+<span class="sd">        &gt;&gt;&gt; dag = BayesianNetwork([(&quot;X&quot;, &quot;Y&quot;), (&quot;Z&quot;, &quot;X&quot;), (&quot;Z&quot;, &quot;Y&quot;)])</span>
+<span class="sd">        &gt;&gt;&gt; dag.get_minimal_adjustment_set(X=&quot;X&quot;, Y=&quot;Y&quot;)</span>
+<span class="sd">        {&quot;Z&quot;}</span>
 
 <span class="sd">        References</span>
 <span class="sd">        ----------</span>
@@ -779,7 +753,14 @@ <h3>Related Topics</h3>
 
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diff --git a/_modules/pgmpy/inference/EliminationOrder.html b/_modules/pgmpy/inference/EliminationOrder.html
index 70c4c3c..25c52f1 100644
--- a/_modules/pgmpy/inference/EliminationOrder.html
+++ b/_modules/pgmpy/inference/EliminationOrder.html
@@ -251,7 +251,14 @@ <h3>Related Topics</h3>
 
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diff --git a/_modules/pgmpy/inference/ExactInference.html b/_modules/pgmpy/inference/ExactInference.html
index 1864728..1b299fa 100644
--- a/_modules/pgmpy/inference/ExactInference.html
+++ b/_modules/pgmpy/inference/ExactInference.html
@@ -1325,7 +1325,14 @@ <h3>Related Topics</h3>
 
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@@ -1352,21 +1359,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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diff --git a/_modules/pgmpy/inference/dbn_inference.html b/_modules/pgmpy/inference/dbn_inference.html
index 7681dcf..d748992 100644
--- a/_modules/pgmpy/inference/dbn_inference.html
+++ b/_modules/pgmpy/inference/dbn_inference.html
@@ -563,7 +563,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
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+<script>
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diff --git a/_modules/pgmpy/metrics/bn_inference.html b/_modules/pgmpy/metrics/bn_inference.html
index 32bf016..6bb0dd4 100644
--- a/_modules/pgmpy/metrics/bn_inference.html
+++ b/_modules/pgmpy/metrics/bn_inference.html
@@ -92,7 +92,7 @@ <h1>Source code for pgmpy.metrics.bn_inference</h1><div class="highlight"><pre>
         <span class="n">current_no</span> <span class="o">=</span> <span class="n">vec_translate</span><span class="p">(</span><span class="n">current_val</span><span class="p">,</span> <span class="n">cpd</span><span class="o">.</span><span class="n">name_to_no</span><span class="p">[</span><span class="n">current</span><span class="p">])</span>
 
         <span class="c1"># conditional dependencies E of the probed variable</span>
-        <span class="n">evidence</span> <span class="o">=</span> <span class="n">cpd</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+        <span class="n">evidence</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">cpd</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="k">if</span> <span class="n">var</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span><span class="p">]</span>
         <span class="n">evidence_idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">ordering</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">ev</span><span class="p">)</span> <span class="k">for</span> <span class="n">ev</span> <span class="ow">in</span> <span class="n">evidence</span><span class="p">]</span>
         <span class="n">evidence_val</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:,</span> <span class="n">evidence_idx</span><span class="p">]</span>
         <span class="n">evidence_no</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">evidence_val</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
@@ -107,7 +107,7 @@ <h1>Source code for pgmpy.metrics.bn_inference</h1><div class="highlight"><pre>
             <span class="n">unique</span><span class="p">,</span> <span class="n">inverse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">evidence_no</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">return_inverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
             <span class="n">unique</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">unique</span><span class="p">]</span>
             <span class="n">state_to_index</span><span class="p">,</span> <span class="n">index_to_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_compute_reduce_maps</span><span class="p">(</span>
-                <span class="n">variable</span><span class="o">=</span><span class="n">node</span><span class="p">,</span> <span class="n">state_combinations</span><span class="o">=</span><span class="n">unique</span>
+                <span class="n">variable</span><span class="o">=</span><span class="n">node</span><span class="p">,</span> <span class="n">evidence</span><span class="o">=</span><span class="n">evidence</span><span class="p">,</span> <span class="n">state_combinations</span><span class="o">=</span><span class="n">unique</span>
             <span class="p">)</span>
             <span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
                 <span class="p">[</span><span class="n">index_to_weight</span><span class="p">[</span><span class="n">state_to_index</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">u</span><span class="p">)]]</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">unique</span><span class="p">]</span>
@@ -236,7 +236,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
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+</script>
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     <div class="searchformwrapper">
@@ -263,21 +270,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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diff --git a/_modules/pgmpy/metrics/metrics.html b/_modules/pgmpy/metrics/metrics.html
index 621d245..6f7c438 100644
--- a/_modules/pgmpy/metrics/metrics.html
+++ b/_modules/pgmpy/metrics/metrics.html
@@ -40,7 +40,7 @@ <h1>Source code for pgmpy.metrics.metrics</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">pgmpy.models</span> <span class="kn">import</span> <span class="n">BayesianNetwork</span>
 
 
-<div class="viewcode-block" id="correlation_score"><a class="viewcode-back" href="../../../metrics/metrics.html#pgmpy.metrics.correlation_score">[docs]</a><span class="k">def</span> <span class="nf">correlation_score</span><span class="p">(</span>
+<div class="viewcode-block" id="correlation_score"><a class="viewcode-back" href="../../../metrics/metrics.html#pgmpy.metrics.metrics.correlation_score">[docs]</a><span class="k">def</span> <span class="nf">correlation_score</span><span class="p">(</span>
     <span class="n">model</span><span class="p">,</span>
     <span class="n">data</span><span class="p">,</span>
     <span class="n">test</span><span class="o">=</span><span class="s2">&quot;chi_square&quot;</span><span class="p">,</span>
@@ -174,7 +174,7 @@ <h1>Source code for pgmpy.metrics.metrics</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">metric</span></div>
 
 
-<div class="viewcode-block" id="log_likelihood_score"><a class="viewcode-back" href="../../../metrics/metrics.html#pgmpy.metrics.log_likelihood_score">[docs]</a><span class="k">def</span> <span class="nf">log_likelihood_score</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+<div class="viewcode-block" id="log_likelihood_score"><a class="viewcode-back" href="../../../metrics/metrics.html#pgmpy.metrics.metrics.log_likelihood_score">[docs]</a><span class="k">def</span> <span class="nf">log_likelihood_score</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes the log-likelihood of a given dataset i.e. P(data | model).</span>
 
@@ -219,7 +219,7 @@ <h1>Source code for pgmpy.metrics.metrics</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">BayesianModelProbability</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="structure_score"><a class="viewcode-back" href="../../../metrics/metrics.html#pgmpy.metrics.structure_score">[docs]</a><span class="k">def</span> <span class="nf">structure_score</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">scoring_method</span><span class="o">=</span><span class="s2">&quot;bic&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<div class="viewcode-block" id="structure_score"><a class="viewcode-back" href="../../../metrics/metrics.html#pgmpy.metrics.metrics.structure_score">[docs]</a><span class="k">def</span> <span class="nf">structure_score</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">scoring_method</span><span class="o">=</span><span class="s2">&quot;bic&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Uses the standard model scoring methods to give a score for each structure.</span>
 <span class="sd">    The score doesn&#39;t have very straight forward interpretebility but can be</span>
@@ -341,7 +341,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -368,21 +375,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
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diff --git a/_modules/pgmpy/models/BayesianNetwork.html b/_modules/pgmpy/models/BayesianNetwork.html
index 6c87025..8b463c5 100644
--- a/_modules/pgmpy/models/BayesianNetwork.html
+++ b/_modules/pgmpy/models/BayesianNetwork.html
@@ -548,8 +548,7 @@ <h1>Source code for pgmpy.models.BayesianNetwork</h1><div class="highlight"><pre
         <span class="n">data</span><span class="p">,</span>
         <span class="n">estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
         <span class="n">state_names</span><span class="o">=</span><span class="p">[],</span>
-        <span class="n">complete_samples_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
     <span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -575,15 +574,9 @@ <h1>Source code for pgmpy.models.BayesianNetwork</h1><div class="highlight"><pre
 <span class="sd">            that the variable can take. If unspecified, the observed values</span>
 <span class="sd">            in the data set are taken to be the only possible states.</span>
 
-<span class="sd">        complete_samples_only: bool (default `True`)</span>
-<span class="sd">            Specifies how to deal with missing data, if present. If set to `True` all rows</span>
-<span class="sd">            that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,</span>
-<span class="sd">            every row where neither the variable nor its parents are `np.NaN` is used.</span>
-
-<span class="sd">        n_jobs: int (default: -1)</span>
-<span class="sd">            Number of threads/processes to use for estimation. It improves speed only</span>
-<span class="sd">            for large networks (&gt;100 nodes). For smaller networks might reduce</span>
-<span class="sd">            performance.</span>
+<span class="sd">        n_jobs: int (default: 1)</span>
+<span class="sd">            Number of threads/processes to use for estimation. Using n_jobs &gt; 1</span>
+<span class="sd">            for small models or datasets might be slower.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -615,12 +608,11 @@ <h1>Source code for pgmpy.models.BayesianNetwork</h1><div class="highlight"><pre
             <span class="bp">self</span><span class="p">,</span>
             <span class="n">data</span><span class="p">,</span>
             <span class="n">state_names</span><span class="o">=</span><span class="n">state_names</span><span class="p">,</span>
-            <span class="n">complete_samples_only</span><span class="o">=</span><span class="n">complete_samples_only</span><span class="p">,</span>
         <span class="p">)</span>
         <span class="n">cpds_list</span> <span class="o">=</span> <span class="n">_estimator</span><span class="o">.</span><span class="n">get_parameters</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">add_cpds</span><span class="p">(</span><span class="o">*</span><span class="n">cpds_list</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="BayesianNetwork.fit_update"><a class="viewcode-back" href="../../../models/bayesiannetwork.html#pgmpy.models.BayesianNetwork.BayesianNetwork.fit_update">[docs]</a>    <span class="k">def</span> <span class="nf">fit_update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">n_prev_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
+<div class="viewcode-block" id="BayesianNetwork.fit_update"><a class="viewcode-back" href="../../../models/bayesiannetwork.html#pgmpy.models.BayesianNetwork.BayesianNetwork.fit_update">[docs]</a>    <span class="k">def</span> <span class="nf">fit_update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">n_prev_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Method to update the parameters of the BayesianNetwork with more data.</span>
 <span class="sd">        Internally, uses BayesianEstimator with dirichlet prior, and uses</span>
@@ -636,10 +628,9 @@ <h1>Source code for pgmpy.models.BayesianNetwork</h1><div class="highlight"><pre
 <span class="sd">            This parameter determines how much weight should the new data be given.</span>
 <span class="sd">            If None, n_prev_samples = nrow(data).</span>
 
-<span class="sd">        n_jobs: int (default: -1)</span>
-<span class="sd">            Number of threads/processes to use for estimation. It improves speed only</span>
-<span class="sd">            for large networks (&gt;100 nodes). For smaller networks might reduce</span>
-<span class="sd">            performance.</span>
+<span class="sd">        n_jobs: int (default: 1)</span>
+<span class="sd">            Number of threads/processes to use for estimation. Using n_jobs &gt; 1</span>
+<span class="sd">            for small models or datasets might be slower.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -1516,7 +1507,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -1543,21 +1541,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
-      (function() {
-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
-        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/models/DynamicBayesianNetwork.html b/_modules/pgmpy/models/DynamicBayesianNetwork.html
index 0a3d883..c73193d 100644
--- a/_modules/pgmpy/models/DynamicBayesianNetwork.html
+++ b/_modules/pgmpy/models/DynamicBayesianNetwork.html
@@ -1253,7 +1253,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -1280,21 +1287,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', '405910108']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
-      (function() {
-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
-        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/models/FactorGraph.html b/_modules/pgmpy/models/FactorGraph.html
index 99044a6..3de89e4 100644
--- a/_modules/pgmpy/models/FactorGraph.html
+++ b/_modules/pgmpy/models/FactorGraph.html
@@ -560,7 +560,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -587,21 +594,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
-      (function() {
-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
-        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/models/MarkovChain.html b/_modules/pgmpy/models/MarkovChain.html
index 2e46d5e..3bc0c1b 100644
--- a/_modules/pgmpy/models/MarkovChain.html
+++ b/_modules/pgmpy/models/MarkovChain.html
@@ -589,7 +589,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -616,21 +623,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
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-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
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-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/models/MarkovNetwork.html b/_modules/pgmpy/models/MarkovNetwork.html
index f0a4e28..56cd079 100644
--- a/_modules/pgmpy/models/MarkovNetwork.html
+++ b/_modules/pgmpy/models/MarkovNetwork.html
@@ -882,7 +882,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -909,21 +916,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', '405910108']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
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-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
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-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/models/NoisyOrModel.html b/_modules/pgmpy/models/NoisyOrModel.html
index 3e84345..5a12f1f 100644
--- a/_modules/pgmpy/models/NoisyOrModel.html
+++ b/_modules/pgmpy/models/NoisyOrModel.html
@@ -236,7 +236,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -263,21 +270,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
-      (function() {
-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
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-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/models/SEM.html b/_modules/pgmpy/models/SEM.html
index 134c70b..70602ca 100644
--- a/_modules/pgmpy/models/SEM.html
+++ b/_modules/pgmpy/models/SEM.html
@@ -1472,7 +1472,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -1499,21 +1506,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
-      (function() {
-        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
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-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/readwrite/BIF.html b/_modules/pgmpy/readwrite/BIF.html
index b96ef9f..a46e822 100644
--- a/_modules/pgmpy/readwrite/BIF.html
+++ b/_modules/pgmpy/readwrite/BIF.html
@@ -799,7 +799,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -826,21 +833,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', '405910108']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
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-        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/readwrite/PomdpX.html b/_modules/pgmpy/readwrite/PomdpX.html
index c8a5eac..aa0fe1a 100644
--- a/_modules/pgmpy/readwrite/PomdpX.html
+++ b/_modules/pgmpy/readwrite/PomdpX.html
@@ -734,7 +734,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -761,21 +768,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
-      _gaq.push(['_setAllowLinker', true]);
-      _gaq.push(['_trackPageview']);
-
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-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/readwrite/UAI.html b/_modules/pgmpy/readwrite/UAI.html
index 6397140..4f7c001 100644
--- a/_modules/pgmpy/readwrite/UAI.html
+++ b/_modules/pgmpy/readwrite/UAI.html
@@ -581,7 +581,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -608,21 +615,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
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-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/readwrite/XMLBIF.html b/_modules/pgmpy/readwrite/XMLBIF.html
index 24e0b11..7f127a6 100644
--- a/_modules/pgmpy/readwrite/XMLBIF.html
+++ b/_modules/pgmpy/readwrite/XMLBIF.html
@@ -595,7 +595,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-HCFR07M31W');
+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -622,21 +629,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
-      var _gaq = _gaq || [];
-      _gaq.push(['_setAccount', 'UA-177825880-1']);
-      _gaq.push(['_setDomainName', 'none']);
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-      })();
-
-    </script>
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/_modules/pgmpy/readwrite/XMLBeliefNetwork.html b/_modules/pgmpy/readwrite/XMLBeliefNetwork.html
index d498fdf..eaade5b 100644
--- a/_modules/pgmpy/readwrite/XMLBeliefNetwork.html
+++ b/_modules/pgmpy/readwrite/XMLBeliefNetwork.html
@@ -532,7 +532,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
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+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
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+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -559,21 +566,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
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diff --git a/_modules/pgmpy/sampling/Sampling.html b/_modules/pgmpy/sampling/Sampling.html
index ca2d01b..2583cce 100644
--- a/_modules/pgmpy/sampling/Sampling.html
+++ b/_modules/pgmpy/sampling/Sampling.html
@@ -145,7 +145,7 @@ <h1>Source code for pgmpy.sampling.Sampling</h1><div class="highlight"><pre>
                     <span class="p">)</span>
                     <span class="n">unique</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">unique</span><span class="p">]</span>
                     <span class="n">state_to_index</span><span class="p">,</span> <span class="n">index_to_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_compute_reduce_maps</span><span class="p">(</span>
-                        <span class="n">variable</span><span class="o">=</span><span class="n">node</span><span class="p">,</span> <span class="n">state_combinations</span><span class="o">=</span><span class="n">unique</span>
+                        <span class="n">variable</span><span class="o">=</span><span class="n">node</span><span class="p">,</span> <span class="n">evidence</span><span class="o">=</span><span class="n">evidence</span><span class="p">,</span> <span class="n">state_combinations</span><span class="o">=</span><span class="n">unique</span>
                     <span class="p">)</span>
                     <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">get_backend</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;numpy&quot;</span><span class="p">:</span>
                         <span class="n">weight_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">state_to_index</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">unique</span><span class="p">])[</span>
@@ -377,7 +377,7 @@ <h1>Source code for pgmpy.sampling.Sampling</h1><div class="highlight"><pre>
                 <span class="p">)</span>
                 <span class="n">unique</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">unique</span><span class="p">]</span>
                 <span class="n">state_to_index</span><span class="p">,</span> <span class="n">index_to_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_compute_reduce_maps</span><span class="p">(</span>
-                    <span class="n">variable</span><span class="o">=</span><span class="n">node</span><span class="p">,</span> <span class="n">state_combinations</span><span class="o">=</span><span class="n">unique</span>
+                    <span class="n">variable</span><span class="o">=</span><span class="n">node</span><span class="p">,</span> <span class="n">evidence</span><span class="o">=</span><span class="n">evidence</span><span class="p">,</span> <span class="n">state_combinations</span><span class="o">=</span><span class="n">unique</span>
                 <span class="p">)</span>
                 <span class="n">weight_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">state_to_index</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">u</span><span class="p">)]</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">unique</span><span class="p">])[</span>
                     <span class="n">inverse</span>
@@ -696,7 +696,14 @@ <h3>Related Topics</h3>
 
 <script async src="https://media.ethicalads.io/media/client/ethicalads.min.js"></script>
 
-<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div>
+<div data-ea-publisher="pgmpyorg" data-ea-type="image" data-ea-style="horizontal"></div><script async src="https://www.googletagmanager.com/gtag/js?id=G-HCFR07M31W"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
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+  gtag('js', new Date());
+
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+</script>
 <div id="searchbox" style="display: none" role="search">
   <h3 id="searchlabel">Quick search</h3>
     <div class="searchformwrapper">
@@ -723,21 +730,5 @@ <h3 id="searchlabel">Quick search</h3>
     
 
     
-    <script type="text/javascript">
-
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 </html>
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diff --git a/base.html b/base.html
index e36e0e4..32104ba 100644
--- a/base.html
+++ b/base.html
@@ -284,7 +284,7 @@ <h1>Base Structure Classes<a class="headerlink" href="#base-structure-classes" t
 <dd class="field-even"><p><strong>Modified DAG</strong> – A new instance of DAG modified by the do-operator</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
@@ -316,7 +316,7 @@ <h1>Base Structure Classes<a class="headerlink" href="#base-structure-classes" t
 <dd class="field-even"><p><strong>Ancestral Graph</strong></p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
@@ -475,7 +475,7 @@ <h1>Base Structure Classes<a class="headerlink" href="#base-structure-classes" t
 <dd class="field-even"><p><strong>Random DAG</strong> – The randomly generated DAG.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
diff --git a/base/base.html b/base/base.html
index cc46ca0..946718d 100644
--- a/base/base.html
+++ b/base/base.html
@@ -286,7 +286,7 @@ <h2>Directed Acyclic Graph (DAG)<a class="headerlink" href="#directed-acyclic-gr
 <dd class="field-even"><p><strong>Modified DAG</strong> – A new instance of DAG modified by the do-operator</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
@@ -318,7 +318,7 @@ <h2>Directed Acyclic Graph (DAG)<a class="headerlink" href="#directed-acyclic-gr
 <dd class="field-even"><p><strong>Ancestral Graph</strong></p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
@@ -477,7 +477,7 @@ <h2>Directed Acyclic Graph (DAG)<a class="headerlink" href="#directed-acyclic-gr
 <dd class="field-even"><p><strong>Random DAG</strong> – The randomly generated DAG.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
diff --git a/exact_infer/causal.html b/exact_infer/causal.html
index 817f460..9111a5d 100644
--- a/exact_infer/causal.html
+++ b/exact_infer/causal.html
@@ -38,40 +38,34 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 <dl class="py class">
 <dt class="sig sig-object py" id="pgmpy.inference.CausalInference.CausalInference">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pgmpy.inference.CausalInference.</span></span><span class="sig-name descname"><span class="pre">CausalInference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">set_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/inference/CausalInference.html#CausalInference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.inference.CausalInference.CausalInference" title="Permalink to this definition">¶</a></dt>
-<dd><p>This is an inference class for performing Causal Inference over Bayesian Networks or Structural Equation Models.</p>
-<dl class="simple">
-<dt>This class will accept queries of the form: P(Y | do(X)) and utilize its methods to provide an estimand which:</dt><dd><ul class="simple">
-<li><p>Identifies adjustment variables</p></li>
-<li><p>Backdoor Adjustment</p></li>
-<li><p>Front Door Adjustment</p></li>
-<li><p>Instrumental Variable Adjustment</p></li>
-</ul>
-</dd>
-</dl>
+<dd><p>This is an inference class for performing Causal Inference over Bayesian
+Networks or Structural Equation Models.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<em>CausalGraph</em>) – The model that we’ll perform inference over.</p></li>
-<li><p><strong>set_nodes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>[</em><em>node:str</em><em>] or </em><em>None</em>) – A list (or set/tuple) of nodes in the Bayesian Network which have been set to a specific value per the
-do-operator.</p></li>
+<li><p><strong>model</strong> (<a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG"><em>pgmpy.base.DAG</em></a><em> | </em><em>pgmpy.models.BayesianNetwork</em>) – The model that we’ll perform inference over.</p></li>
+<li><p><strong>set_nodes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>[</em><em>node:str</em><em>] or </em><em>None</em>) – A list (or set/tuple) of nodes in the Bayesian Network which have been
+set to a specific value per the do-operator.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<p>Create a small Bayesian Network.
-&gt;&gt;&gt; from pgmpy.models import BayesianNetwork
-&gt;&gt;&gt; game = BayesianNetwork([(‘X’, ‘A’),
-…                         (‘A’, ‘Y’),
-…                         (‘A’, ‘B’)])</p>
-<p>Load the graph into the CausalInference object to make causal queries.
-&gt;&gt;&gt; from pgmpy.inference.CausalInference import CausalInference
-&gt;&gt;&gt; inference = CausalInference(game)
-&gt;&gt;&gt; inference.get_all_backdoor_adjustment_sets(X=”X”, Y=”Y”)
-&gt;&gt;&gt; inference.get_all_frontdoor_adjustment_sets(X=”X”, Y=”Y”)</p>
+<p>Create a small Bayesian Network.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pgmpy.models</span> <span class="kn">import</span> <span class="n">BayesianNetwork</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">game</span> <span class="o">=</span> <span class="n">BayesianNetwork</span><span class="p">([(</span><span class="s1">&#39;X&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">),</span>
+<span class="gp">... </span>                        <span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;Y&#39;</span><span class="p">),</span>
+<span class="gp">... </span>                        <span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">)])</span>
+</pre></div>
+</div>
+<p>Load the graph into the CausalInference object to make causal queries.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pgmpy.inference.CausalInference</span> <span class="kn">import</span> <span class="n">CausalInference</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inference</span> <span class="o">=</span> <span class="n">CausalInference</span><span class="p">(</span><span class="n">game</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inference</span><span class="o">.</span><span class="n">get_all_backdoor_adjustment_sets</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="s2">&quot;Y&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inference</span><span class="o">.</span><span class="n">get_all_frontdoor_adjustment_sets</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="s2">&quot;Y&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
 <p class="rubric">References</p>
 <p>‘Causality: Models, Reasoning, and Inference’ - Judea Pearl (2000)</p>
-<p>Many thanks to &#64;ijmbarr for their implementation of Causal Graphical models available. It served as an invaluable
-reference. Available on GitHub: <a class="reference external" href="https://github.com/ijmbarr/causalgraphicalmodels">https://github.com/ijmbarr/causalgraphicalmodels</a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="pgmpy.inference.CausalInference.CausalInference.estimate_ate">
 <span class="sig-name descname"><span class="pre">estimate_ate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimand_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'smallest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/inference/CausalInference.html#CausalInference.estimate_ate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.inference.CausalInference.CausalInference.estimate_ate" title="Permalink to this definition">¶</a></dt>
@@ -79,8 +73,8 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Intervention Variable</p></li>
-<li><p><strong>Y</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Target Variable</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The cause/exposure variables.</p></li>
+<li><p><strong>Y</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The outcome variable</p></li>
 <li><p><strong>data</strong> (<em>pandas.DataFrame</em>) – All observed data for this Bayesian Network.</p></li>
 <li><p><strong>estimand_strategy</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#frozenset" title="(in Python v3.12)"><em>frozenset</em></a>) – <p>Either specify a specific backdoor adjustment set or a strategy.
 The available options are:</p>
@@ -145,21 +139,12 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 </ol>
 </dd>
 </dl>
-<div class="admonition-todo admonition" id="id1">
-<p class="admonition-title">Todo</p>
-<ul class="simple">
-<li><p>Backdoors are great, but the most general things we could implement would be Ilya Shpitser’s ID and
-IDC algorithms. See [his Ph.D. thesis for a full explanation]
-(<a class="reference external" href="https://ftp.cs.ucla.edu/pub/stat_ser/shpitser-thesis.pdf">https://ftp.cs.ucla.edu/pub/stat_ser/shpitser-thesis.pdf</a>). After doing a little reading it is clear
-that we do not need to immediatly implement this.  However, in order for us to truly account for
-unobserved variables, we will need not only these algorithms, but a more general implementation of a DAG.
-Most DAGs do not allow for bidirected edges, but it is an important piece of notation which Pearl and
-Shpitser use to denote graphs with latent variables.</p></li>
-</ul>
-</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Intervention Variable</p>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The cause/exposure variables.</p></li>
+<li><p><strong>Y</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The outcome variable.</p></li>
+</ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
@@ -178,8 +163,6 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 <span class="go">frozenset()</span>
 </pre></div>
 </div>
-<p class="rubric">References</p>
-<p>“Causality: Models, Reasoning, and Inference”, Judea Pearl (2000). p.79.</p>
 </dd></dl>
 
 <dl class="py method">
@@ -195,21 +178,19 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 </dd>
 </dl>
 <dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>frozenset</strong></p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The cause/exposure variables.</p></li>
+<li><p><strong>Y</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The outcome variable</p></li>
+</ul>
 </dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>a frozenset of frozensets</p>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>frozenset</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>a frozenset of frozensets</p>
 </dd>
 </dl>
-<p class="rubric">References</p>
-<p>Causality: Models, Reasoning, and Inference, Judea Pearl (2000). p.82.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="pgmpy.inference.CausalInference.CausalInference.get_distribution">
-<span class="sig-name descname"><span class="pre">get_distribution</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/inference/CausalInference.html#CausalInference.get_distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.inference.CausalInference.CausalInference.get_distribution" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a string representing the factorized distribution implied by the CGM.</p>
 </dd></dl>
 
 <dl class="py method">
@@ -233,6 +214,13 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 </dd>
 </dl>
 <p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pgmpy.models</span> <span class="kn">import</span> <span class="n">BayesianNetwork</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pgmpy.inference</span> <span class="kn">import</span> <span class="n">CausalInference</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dag</span> <span class="o">=</span> <span class="n">BayesianNetwork</span><span class="p">([(</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;Z&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;Z&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dag</span><span class="o">.</span><span class="n">get_minimal_adjustment_set</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="s2">&quot;Y&quot;</span><span class="p">)</span>
+<span class="go">{&quot;Z&quot;}</span>
+</pre></div>
+</div>
 <p class="rubric">References</p>
 <p>[1] Perkovic, Emilija, et al. “Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs.” The Journal of Machine Learning Research 18.1 (2017): 8132-8193.</p>
 </dd></dl>
@@ -310,9 +298,9 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Intervention Variable</p></li>
-<li><p><strong>Y</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Target Variable</p></li>
-<li><p><strong>Z</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#set" title="(in Python v3.12)"><em>set</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em>) – Adjustment variables</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The cause/exposure variables.</p></li>
+<li><p><strong>Y</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The outcome variable.</p></li>
+<li><p><strong>Z</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>array-like</em><em>)</em>) – List of adjustment variables.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -341,9 +329,9 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Intervention Variable</p></li>
-<li><p><strong>Y</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Target Variable</p></li>
-<li><p><strong>Z</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#set" title="(in Python v3.12)"><em>set</em></a>) – Adjustment variables</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The cause/exposure variables.</p></li>
+<li><p><strong>Y</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> (</em><em>variable name</em><em>)</em>) – The outcome variable.</p></li>
+<li><p><strong>Z</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>array-like</em><em>)</em>) – List of adjustment variables.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -397,20 +385,6 @@ <h1>Causal Inference<a class="headerlink" href="#causal-inference" title="Permal
 </div>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="pgmpy.inference.CausalInference.CausalInference.simple_decision">
-<span class="sig-name descname"><span class="pre">simple_decision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">adjustment_sets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/inference/CausalInference.html#CausalInference.simple_decision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.inference.CausalInference.CausalInference.simple_decision" title="Permalink to this definition">¶</a></dt>
-<dd><p>Selects the smallest set from provided adjustment sets.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>adjustment_sets</strong> (<em>iterable</em>) – A frozenset or list of valid adjustment sets</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#frozenset" title="(in Python v3.12)">frozenset</a></p>
-</dd>
-</dl>
-</dd></dl>
-
 </dd></dl>
 
 </section>
diff --git a/exact_infer/model_testing.html b/exact_infer/model_testing.html
index 8b0986f..73ac84b 100644
--- a/exact_infer/model_testing.html
+++ b/exact_infer/model_testing.html
@@ -55,7 +55,7 @@ <h1>Model Testing<a class="headerlink" href="#model-testing" title="Permalink to
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<em>Instance</em><em> of </em><em>pgmpy.base.DAG</em><em> or </em><em>pgmpy.models.BayesianNetwork</em>) – The model which needs to be tested.</p></li>
+<li><p><strong>model</strong> (<em>Instance</em><em> of </em><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG"><em>pgmpy.base.DAG</em></a><em> or </em><em>pgmpy.models.BayesianNetwork</em>) – The model which needs to be tested.</p></li>
 <li><p><strong>data</strong> (<em>pandas.DataFrame instance</em>) – The dataset against which to test the model structure.</p></li>
 <li><p><strong>test</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>function</em>) – The statistical tests to use for determining whether the variables in data
 are correlated or not. For discrete variables, the options are: 1) chi_square
@@ -100,7 +100,7 @@ <h1>Model Testing<a class="headerlink" href="#model-testing" title="Permalink to
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<em>pgmpy.base.DAG</em><em> or </em><em>pgmpy.models.BayesianNetwork instance</em>) – The model whose score needs to be computed.</p></li>
+<li><p><strong>model</strong> (<a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG"><em>pgmpy.base.DAG</em></a><em> or </em><em>pgmpy.models.BayesianNetwork instance</em>) – The model whose score needs to be computed.</p></li>
 <li><p><strong>data</strong> (<em>pd.DataFrame instance</em>) – The dataset against which to score the model.</p></li>
 </ul>
 </dd>
@@ -127,7 +127,7 @@ <h1>Model Testing<a class="headerlink" href="#model-testing" title="Permalink to
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<em>pgmpy.base.DAG</em><em> or </em><em>pgmpy.models.BayesianNetwork instance</em>) – The model whose score needs to be computed.</p></li>
+<li><p><strong>model</strong> (<a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG"><em>pgmpy.base.DAG</em></a><em> or </em><em>pgmpy.models.BayesianNetwork instance</em>) – The model whose score needs to be computed.</p></li>
 <li><p><strong>data</strong> (<em>pd.DataFrame instance</em>) – The dataset against which to score the model.</p></li>
 <li><p><strong>scoring_method</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> ( </em><em>k2</em><em> | </em><em>bdeu</em><em> | </em><em>bds</em><em> | </em><em>bic</em><em> )</em>) – The following four scoring methods are supported currently: 1) K2Score
 2) BDeuScore 3) BDsScore 4) BicScore</p></li>
diff --git a/examples.html b/examples.html
index f3be365..ae0d9f8 100644
--- a/examples.html
+++ b/examples.html
@@ -15,7 +15,7 @@
     <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Example Using the Earthquake network" href="examples/Earthquake.html" />
+    <link rel="next" title="Tutorial Notebooks" href="tutorial.html" />
     <link rel="prev" title="XMLBeliefNetwork" href="readwrite/xmlbelief.html" />
    
   <link rel="stylesheet" href="_static/custom.css" type="text/css" />
@@ -73,19 +73,7 @@ <h3>Navigation</h3>
 <li class="toctree-l1"><a class="reference internal" href="structure_estimator/base.html">Structure Learning</a></li>
 <li class="toctree-l1"><a class="reference internal" href="metrics/metrics.html">Metrics for testing models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="readwrite/base.html">Reading/Writing to File</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Example Notebooks</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="examples/Earthquake.html">Example Using the Earthquake network</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Monty%20Hall%20Problem.html">Monty Hall Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Creating%20a%20Discrete%20Bayesian%20Network.html">Creating discrete Bayesian Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Inference%20in%20Discrete%20Bayesian%20Networks.html">Inference in Discrete Bayesian Network</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Causal%20Games.html">Causal Games</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Causal%20Inference.html">Causal Inference Examples</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Learning%20Parameters%20in%20Discrete%20Bayesian%20Networks.html">Parameter Learning in Discrete Bayesian Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Structure%20Learning%20in%20Bayesian%20Networks.html">Structure Learning in Bayesian Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Structure%20Learning%20with%20Chow-Liu.html">Learning Tree Structure from Data using the Chow-Liu Algorithm</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Structure%20Learning%20with%20TAN.html">Learning Tree-augmented Naive Bayes (TAN) Structure from Data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Simulating%20Data.html">Normal Bayesian Network (no time variation)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/Extending%20pgmpy.html">Extending pgmpy</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Example Notebooks</a><ul class="simple">
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorial.html">Tutorial Notebooks</a></li>
@@ -96,7 +84,7 @@ <h3>Related Topics</h3>
 <ul>
   <li><a href="index.html">Documentation overview</a><ul>
       <li>Previous: <a href="readwrite/xmlbelief.html" title="previous chapter">XMLBeliefNetwork</a></li>
-      <li>Next: <a href="examples/Earthquake.html" title="next chapter">Example Using the Earthquake network</a></li>
+      <li>Next: <a href="tutorial.html" title="next chapter">Tutorial Notebooks</a></li>
   </ul></li>
 </ul>
 </div>
diff --git a/factors/discrete.html b/factors/discrete.html
index 801db56..ff6327d 100644
--- a/factors/discrete.html
+++ b/factors/discrete.html
@@ -166,7 +166,7 @@ <h1>Discrete<a class="headerlink" href="#discrete" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="pgmpy.factors.discrete.CPD.TabularCPD.get_random">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_random</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">variable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evidence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cardinality</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/factors/discrete/CPD.html#TabularCPD.get_random"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.factors.discrete.CPD.TabularCPD.get_random" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_random</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">variable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evidence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cardinality</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/factors/discrete/CPD.html#TabularCPD.get_random"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.factors.discrete.CPD.TabularCPD.get_random" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a TabularCPD instance with random values on <cite>variable</cite> with
 parents/evidence <cite>evidence</cite> with cardinality/number of states as given
 in <cite>cardinality</cite>.</p>
diff --git a/genindex.html b/genindex.html
index fcd0906..29466ca 100644
--- a/genindex.html
+++ b/genindex.html
@@ -508,11 +508,7 @@ <h2 id="G">G</h2>
 </li>
       </ul></li>
       <li><a href="approx_infer/approx_infer.html#pgmpy.inference.ApproxInference.ApproxInference.get_distribution">get_distribution() (pgmpy.inference.ApproxInference.ApproxInference method)</a>
-
-      <ul>
-        <li><a href="exact_infer/causal.html#pgmpy.inference.CausalInference.CausalInference.get_distribution">(pgmpy.inference.CausalInference.CausalInference method)</a>
 </li>
-      </ul></li>
       <li><a href="readwrite/xmlbelief.html#pgmpy.readwrite.XMLBeliefNetwork.XBNReader.get_distributions">get_distributions() (pgmpy.readwrite.XMLBeliefNetwork.XBNReader method)</a>
 </li>
       <li><a href="readwrite/uai.html#pgmpy.readwrite.UAI.UAIReader.get_domain">get_domain() (pgmpy.readwrite.UAI.UAIReader method)</a>
@@ -1467,8 +1463,6 @@ <h2 id="S">S</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="readwrite/xmlbelief.html#pgmpy.readwrite.XMLBeliefNetwork.XBNWriter.set_variables">set_variables() (pgmpy.readwrite.XMLBeliefNetwork.XBNWriter method)</a>
-</li>
-      <li><a href="exact_infer/causal.html#pgmpy.inference.CausalInference.CausalInference.simple_decision">simple_decision() (pgmpy.inference.CausalInference.CausalInference method)</a>
 </li>
       <li><a href="models/bayesiannetwork.html#pgmpy.models.BayesianNetwork.BayesianNetwork.simulate">simulate() (pgmpy.models.BayesianNetwork.BayesianNetwork method)</a>
 
diff --git a/metrics/metrics.html b/metrics/metrics.html
index c22949c..c80ccba 100644
--- a/metrics/metrics.html
+++ b/metrics/metrics.html
@@ -55,7 +55,7 @@
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<em>Instance</em><em> of </em><em>pgmpy.base.DAG</em><em> or </em><em>pgmpy.models.BayesianNetwork</em>) – The model which needs to be tested.</p></li>
+<li><p><strong>model</strong> (<em>Instance</em><em> of </em><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG"><em>pgmpy.base.DAG</em></a><em> or </em><em>pgmpy.models.BayesianNetwork</em>) – The model which needs to be tested.</p></li>
 <li><p><strong>data</strong> (<em>pandas.DataFrame instance</em>) – The dataset against which to test the model structure.</p></li>
 <li><p><strong>test</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>function</em>) – The statistical tests to use for determining whether the variables in data
 are correlated or not. For discrete variables, the options are: 1) chi_square
@@ -100,7 +100,7 @@
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<em>pgmpy.base.DAG</em><em> or </em><em>pgmpy.models.BayesianNetwork instance</em>) – The model whose score needs to be computed.</p></li>
+<li><p><strong>model</strong> (<a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG"><em>pgmpy.base.DAG</em></a><em> or </em><em>pgmpy.models.BayesianNetwork instance</em>) – The model whose score needs to be computed.</p></li>
 <li><p><strong>data</strong> (<em>pd.DataFrame instance</em>) – The dataset against which to score the model.</p></li>
 </ul>
 </dd>
@@ -127,7 +127,7 @@
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<em>pgmpy.base.DAG</em><em> or </em><em>pgmpy.models.BayesianNetwork instance</em>) – The model whose score needs to be computed.</p></li>
+<li><p><strong>model</strong> (<a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG"><em>pgmpy.base.DAG</em></a><em> or </em><em>pgmpy.models.BayesianNetwork instance</em>) – The model whose score needs to be computed.</p></li>
 <li><p><strong>data</strong> (<em>pd.DataFrame instance</em>) – The dataset against which to score the model.</p></li>
 <li><p><strong>scoring_method</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> ( </em><em>k2</em><em> | </em><em>bdeu</em><em> | </em><em>bds</em><em> | </em><em>bic</em><em> )</em>) – The following four scoring methods are supported currently: 1) K2Score
 2) BDeuScore 3) BDsScore 4) BicScore</p></li>
diff --git a/models/bayesiannetwork.html b/models/bayesiannetwork.html
index d2fb138..97cdf79 100644
--- a/models/bayesiannetwork.html
+++ b/models/bayesiannetwork.html
@@ -272,7 +272,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="pgmpy.models.BayesianNetwork.BayesianNetwork.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">complete_samples_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/models/BayesianNetwork.html#BayesianNetwork.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.models.BayesianNetwork.BayesianNetwork.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/models/BayesianNetwork.html#BayesianNetwork.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.models.BayesianNetwork.BayesianNetwork.fit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimates the CPD for each variable based on a given data set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -289,12 +289,8 @@
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states
 that the variable can take. If unspecified, the observed values
 in the data set are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.Nan</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.</p></li>
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: -1</em><em>)</em>) – Number of threads/processes to use for estimation. It improves speed only
-for large networks (&gt;100 nodes). For smaller networks might reduce
-performance.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: 1</em><em>)</em>) – Number of threads/processes to use for estimation. Using n_jobs &gt; 1
+for small models or datasets might be slower.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -321,7 +317,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="pgmpy.models.BayesianNetwork.BayesianNetwork.fit_update">
-<span class="sig-name descname"><span class="pre">fit_update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prev_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/models/BayesianNetwork.html#BayesianNetwork.fit_update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.models.BayesianNetwork.BayesianNetwork.fit_update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fit_update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prev_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/models/BayesianNetwork.html#BayesianNetwork.fit_update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.models.BayesianNetwork.BayesianNetwork.fit_update" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method to update the parameters of the BayesianNetwork with more data.
 Internally, uses BayesianEstimator with dirichlet prior, and uses
 the current CPDs (along with <cite>n_prev_samples</cite>) to compute the pseudo_counts.</p>
@@ -332,9 +328,8 @@
 <li><p><strong>n_prev_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The number of samples/datapoints on which the model was trained before.
 This parameter determines how much weight should the new data be given.
 If None, n_prev_samples = nrow(data).</p></li>
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: -1</em><em>)</em>) – Number of threads/processes to use for estimation. It improves speed only
-for large networks (&gt;100 nodes). For smaller networks might reduce
-performance.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: 1</em><em>)</em>) – Number of threads/processes to use for estimation. Using n_jobs &gt; 1
+for small models or datasets might be slower.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -477,7 +472,7 @@
 <dd class="field-even"><p><strong>Random DAG</strong> – The randomly generated DAG.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
diff --git a/models/dbn.html b/models/dbn.html
index f263e20..cf64ecc 100644
--- a/models/dbn.html
+++ b/models/dbn.html
@@ -520,7 +520,7 @@ <h2>Public Methods<a class="headerlink" href="#public-methods" title="Permalink
 <dd class="field-even"><p><strong>Modified DAG</strong> – A new instance of DAG modified by the do-operator</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
@@ -703,7 +703,7 @@ <h2>Public Methods<a class="headerlink" href="#public-methods" title="Permalink
 <dd class="field-even"><p><strong>Ancestral Graph</strong></p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
@@ -1014,7 +1014,7 @@ <h2>Public Methods<a class="headerlink" href="#public-methods" title="Permalink
 <dd class="field-even"><p><strong>Random DAG</strong> – The randomly generated DAG.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
diff --git a/objects.inv b/objects.inv
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diff --git a/param_estimator/bayesian_est.html b/param_estimator/bayesian_est.html
index b563a1b..1f48f14 100644
--- a/param_estimator/bayesian_est.html
+++ b/param_estimator/bayesian_est.html
@@ -109,7 +109,7 @@ <h1>Bayesian Estimator<a class="headerlink" href="#bayesian-estimator" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="pgmpy.estimators.BayesianEstimator.get_parameters">
-<span class="sig-name descname"><span class="pre">get_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prior_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'BDeu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equivalent_sample_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pseudo_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weighted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/estimators/BayesianEstimator.html#BayesianEstimator.get_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.estimators.BayesianEstimator.get_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prior_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'BDeu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equivalent_sample_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pseudo_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weighted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/estimators/BayesianEstimator.html#BayesianEstimator.get_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.estimators.BayesianEstimator.get_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method to estimate the model parameters (CPDs).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -141,6 +141,10 @@ <h1>Bayesian Estimator<a class="headerlink" href="#bayesian-estimator" title="Pe
 </li>
 </ul>
 </p></li>
+<li><p><strong>equivalent_sample_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Refer <cite>priort_type</cite> for more details.</p></li>
+<li><p><strong>pseudo_counts</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: None</em><em>)</em>) – Refer <cite>priort_type</cite> for more details.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: 1</em><em>)</em>) – Number of jobs to run in parallel. Default: 1 uses all the processors.
+Using n_jobs &gt; 1 for small models might be slower.</p></li>
 <li><p><strong>weighted</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If weighted=True, the data must contain a <cite>_weight</cite> column specifying the
 weight of each datapoint (row). If False, assigns an equal weight to each
 datapoint.</p></li>
diff --git a/param_estimator/em.html b/param_estimator/em.html
index f4e7fa2..74f07fa 100644
--- a/param_estimator/em.html
+++ b/param_estimator/em.html
@@ -55,10 +55,6 @@ <h1>Expectation Maximization (EM)<a class="headerlink" href="#expectation-maximi
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states
 that the variable can take. If unspecified, the observed values in
 the data set are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to
-<cite>True</cite> all rows that contain <cite>np.NaN</cite> somewhere are ignored. If
-<cite>False</cite> then, for each variable, every row where neither the
-variable nor its parents are <cite>np.NaN</cite> is used.</p></li>
 </ul>
 </dd>
 </dl>
@@ -75,7 +71,7 @@ <h1>Expectation Maximization (EM)<a class="headerlink" href="#expectation-maximi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="pgmpy.estimators.ExpectationMaximization.get_parameters">
-<span class="sig-name descname"><span class="pre">get_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">latent_card</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/estimators/EM.html#ExpectationMaximization.get_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.estimators.ExpectationMaximization.get_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">latent_card</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/estimators/EM.html#ExpectationMaximization.get_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.estimators.ExpectationMaximization.get_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method to estimate all model parameters (CPDs) using Expecation Maximization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -87,8 +83,8 @@ <h1>Expectation Maximization (EM)<a class="headerlink" href="#expectation-maximi
 If max_iter is reached, return the last value of parameters.</p></li>
 <li><p><strong>atol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: 1e-08</em><em>)</em>) – The absolute accepted tolerance for checking convergence. If the parameters
 change is less than atol in an iteration, the algorithm will exit.</p></li>
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: -1</em><em>)</em>) – Number of jobs to run in parallel. Default: -1 uses all the processors.
-Suggest to use n_jobs=1 when dataset size is less than 1000.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: 1</em><em>)</em>) – Number of jobs to run in parallel.
+Using n_jobs &gt; 1 for small models or datasets might be slower.</p></li>
 <li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: 1000</em><em>)</em>) – Number of data used to compute weights in a batch.</p></li>
 <li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The random seed to use for generating the intial values.</p></li>
 <li><p><strong>show_progress</strong> (<em>boolean</em><em> (</em><em>default: True</em><em>)</em>) – Whether to show a progress bar for iterations.</p></li>
diff --git a/param_estimator/mle.html b/param_estimator/mle.html
index 5e458ac..e5dc262 100644
--- a/param_estimator/mle.html
+++ b/param_estimator/mle.html
@@ -49,9 +49,6 @@ <h1>Maximum Likelihood Estimator<a class="headerlink" href="#maximum-likelihood-
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states
 that the variable can take. If unspecified, the observed values
 in the data set are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.NaN</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.</p></li>
 </ul>
 </dd>
 </dl>
@@ -116,13 +113,14 @@ <h1>Maximum Likelihood Estimator<a class="headerlink" href="#maximum-likelihood-
 
 <dl class="py method">
 <dt class="sig sig-object py" id="pgmpy.estimators.MLE.MaximumLikelihoodEstimator.get_parameters">
-<span class="sig-name descname"><span class="pre">get_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weighted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/estimators/MLE.html#MaximumLikelihoodEstimator.get_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.estimators.MLE.MaximumLikelihoodEstimator.get_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weighted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pgmpy/estimators/MLE.html#MaximumLikelihoodEstimator.get_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pgmpy.estimators.MLE.MaximumLikelihoodEstimator.get_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method to estimate the model parameters (CPDs) using Maximum Likelihood
 Estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: -1</em><em>)</em>) – Number of jobs to run in parallel. Default: -1 uses all the processors.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default: 1</em><em>)</em>) – Number of jobs to run in parallel. Default: 1 uses all the processors.
+Using n_jobs &gt; 1 for small models might be slower.</p></li>
 <li><p><strong>weighted</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If weighted=True, the data must contain a <cite>_weight</cite> column specifying the
 weight of each datapoint (row). If False, assigns an equal weight to each
 datapoint.</p></li>
diff --git a/searchindex.js b/searchindex.js
index 990cb94..ebd3077 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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Inference", "Bayesian Model Sampling", "Gibbs Sampling", "Base Structure Classes", "Base Model Structures", "Introduction to Probabilitic Graphical Models", "Learning Bayesian Networks from Data", "A Bayesian Network to model the influence of energy consumption on greenhouse gases in Italy", "Bayesian Network", "Causal Bayesian Networks", "Markov Networks", "Exact Inference in Graphical Models", "Approximate Inference in Graphical Models", "Parameterizing with Continuous Variables", "Sampling In Continuous Graphical Models", "Reading and Writing from pgmpy file formats", "Markdown Cells", "Exact Inference", "Belief Propagation", "Causal Inference", "Dynamic Bayesian Network Inference", "Model Testing", "MPLP", "Variable Elimination", "Example Notebooks", "Causal Games", "Causal Inference Examples", "Creating discrete Bayesian Networks", "Example Using the Earthquake network", "Extending pgmpy", "Linear Gaussian Bayesian Networks (GBNs)", "Inference in Discrete Bayesian Network", 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30], "05": [2, 3, 7, 8, 9, 16, 22, 28, 29, 41, 42, 44, 48, 62, 65, 66, 76], "9": [2, 4, 5, 6, 7, 8, 9, 14, 15, 16, 19, 21, 26, 27, 28, 29, 30, 32, 37, 39, 41, 42, 46, 48, 62, 65, 66, 74], "5": [2, 3, 4, 5, 6, 7, 8, 10, 14, 15, 16, 17, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44, 46, 48, 51, 53, 57, 58, 59, 68, 73, 74, 75, 76, 77], "25": [2, 6, 7, 9, 10, 14, 15, 27, 41, 48, 51, 57], "08": [2, 9, 41, 46, 48, 58], "02": [2, 8, 9, 16, 28, 32, 48], "add_cpd": [2, 3, 9, 14, 16, 19, 21, 24, 27, 28, 29, 30, 35, 38, 39, 41, 46, 48], "rec": [2, 8], "dtype": [2, 6, 8, 31, 36, 46, 58, 59, 73, 74, 75, 76], "i8": 2, "likelihood_weighted_sampl": 2, "weight": [2, 4, 5, 8, 20, 24, 31, 37, 46, 48, 55, 57, 58, 59, 60, 77], "compli": 2, "probabilist": [2, 4, 5, 7, 8, 9, 16, 19, 31, 43, 46, 48, 55, 74, 76], "graphic": [2, 4, 5, 7, 9, 19, 20, 48, 55, 74, 76], "principl": [2, 4, 5, 7, 48, 55, 74, 76], "techniqu": [2, 4, 5, 6, 7, 48, 55, 68, 74, 76], "koller": [2, 4, 5, 7, 48, 55, 74, 76], "friedman": [2, 4, 5, 7, 48, 55, 74, 76, 77], "algorithm": [2, 4, 5, 6, 7, 8, 9, 12, 14, 20, 21, 23, 24, 30, 32, 33, 37, 39, 42, 48, 49, 52, 55, 58, 74, 75, 77], "12": [2, 6, 7, 8, 14, 26, 28, 41, 42], "pp": [2, 74], "493": 2, "namedtupl": [2, 3, 51], "correspond": [2, 7, 15, 16, 22, 41, 46, 47, 48, 49, 52, 54], "return_typ": [2, 76], "recarrai": 2, "_weight": [2, 57, 59], "f8": 2, "rejection_sampl": 2, "gibbssampl": 3, "perform": [3, 7, 8, 14, 15, 19, 20, 21, 23, 46, 74], "markovnetwork": [3, 19, 23, 52], "ar": [3, 4, 5, 6, 7, 8, 10, 12, 14, 15, 16, 17, 20, 22, 23, 24, 26, 28, 29, 31, 32, 33, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 64, 68, 73, 74, 75, 76, 77], "inherit": [3, 14, 24, 30, 60], "transit": [3, 51, 63], "initi": [0, 3, 4, 5, 14, 15, 23, 28, 41, 46, 47, 48, 49, 50, 51, 52, 55, 60, 62, 63, 64, 65], "object": [3, 4, 5, 9, 14, 15, 16, 20, 23, 24, 31, 41, 42, 46, 47, 48, 49, 51, 52, 53, 55, 57, 58, 59, 60, 62, 63, 66, 73, 74, 75, 76, 77], "intel_cpd": [3, 19, 41, 46, 48], "sat_cpd": [3, 19, 46], "sat": [3, 4, 5, 9, 19, 46, 48], "95": [3, 8, 9, 16, 28, 29, 46, 62, 63, 65, 66], "8": [3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 19, 21, 26, 27, 28, 29, 30, 31, 32, 34, 37, 38, 39, 41, 42, 46, 47, 48, 55, 63, 65, 66], "evidence_card": [3, 9, 16, 19, 21, 27, 28, 29, 30, 35, 38, 39, 41, 46, 48], "add_nodes_from": [3, 4, 5, 23, 46, 47, 48, 49, 50, 52, 53], "add_edg": [3, 4, 5, 10, 26, 46, 47, 48, 49, 50, 52, 53], "gibbs_chain": 3, "generate_sampl": [3, 15, 51, 55], "start_stat": [3, 51], "version": [3, 10, 17, 26, 48, 51, 65, 68, 76], "self": [3, 4, 5, 15, 20, 30, 41, 42, 46, 48, 51], "assign": [3, 8, 9, 14, 17, 23, 41, 48, 51, 57, 59], "b": [3, 4, 5, 7, 8, 9, 10, 15, 16, 17, 20, 23, 24, 26, 36, 37, 38, 39, 41, 46, 47, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 60, 65, 68, 73, 74, 75, 76, 77], "c": [3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 20, 21, 23, 24, 26, 27, 35, 38, 39, 41, 46, 47, 48, 49, 50, 52, 53, 57, 58, 59, 62, 65, 66, 70, 71, 73, 74, 75, 76, 77], "factor_ab": 3, "factor_cb": 3, "add_factor": [3, 23, 47, 49, 50, 52], "gen": [3, 51], "markov": [3, 4, 5, 6, 8, 15, 16, 20, 45, 46, 48, 49, 63, 64], "chain": [3, 4, 5, 9, 15, 30, 45, 48], "iter": [3, 4, 5, 7, 9, 15, 16, 20, 23, 46, 47, 48, 51, 58, 60, 74], "start": [3, 4, 5, 7, 8, 9, 10, 15, 17, 26, 41, 48, 51, 53, 55, 60, 68, 74], "pass": [3, 4, 5, 9, 16, 17, 20, 22, 23, 28, 32, 42, 44, 46, 47, 48, 50, 51, 52, 55, 76], "chosen": [3, 8, 51, 55], "return_tup": 3, "ebunch": [4, 5, 33, 46, 47, 48, 49, 50, 52, 53, 55], "node": [4, 5, 6, 7, 8, 9, 10, 15, 16, 20, 21, 23, 24, 26, 28, 29, 30, 32, 33, 37, 38, 39, 42, 46, 47, 48, 49, 50, 52, 53, 55, 57, 59, 73, 74, 75, 76, 77], "can": [4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 17, 22, 23, 26, 28, 30, 31, 32, 33, 35, 37, 41, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 60, 68, 73, 74, 75, 76], "either": [4, 5, 8, 10, 17, 20, 26, 31, 32, 41, 46, 48, 55, 57, 60, 74, 76], "cluster": [4, 5, 23, 45], "edg": [4, 5, 7, 8, 9, 10, 16, 20, 23, 24, 26, 28, 29, 32, 33, 38, 39, 41, 46, 47, 48, 49, 50, 52, 53, 55, 62, 64, 65, 66, 73, 74, 75, 76, 77], "depend": [4, 5, 7, 9, 14, 15, 16, 28, 31, 52, 53, 70, 74, 75, 76, 77], "between": [4, 5, 6, 7, 8, 9, 15, 17, 20, 22, 23, 28, 41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 55, 74], "input": [4, 5, 6, 8, 14, 19, 21, 31, 46, 47, 48, 49, 50, 52], "an": [4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 20, 23, 24, 28, 31, 32, 33, 35, 38, 39, 41, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 63, 64, 65, 71, 73, 74, 75, 76, 77], "empti": [4, 5, 10, 17, 46, 47, 48, 49, 50, 51, 52, 73], "creat": [4, 5, 7, 8, 15, 16, 17, 19, 20, 29, 30, 32, 46, 47, 48, 49, 50, 51, 52, 55, 62, 68, 73, 74], "ani": [4, 5, 7, 8, 9, 10, 15, 17, 20, 22, 24, 26, 31, 35, 41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 59, 68, 71, 74, 75, 76, 77], "networkx": [4, 5, 10, 37, 38, 39, 46, 48, 52, 73, 76, 77], "g": [4, 5, 6, 7, 9, 12, 14, 15, 17, 19, 20, 24, 30, 31, 37, 41, 42, 46, 47, 48, 49, 50, 52, 53, 74, 76], "grown": [4, 5, 46, 47, 49, 50, 52], "sever": [4, 5, 8, 46, 51, 52], "wai": [4, 5, 7, 8, 9, 10, 26, 32, 46, 48, 52, 65, 68, 76], "add": [4, 5, 7, 12, 15, 17, 30, 31, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 62, 63, 64, 66, 68, 74], "one": [4, 5, 7, 8, 10, 14, 15, 17, 22, 26, 30, 35, 38, 39, 44, 46, 48, 49, 50, 52, 53, 55, 57, 59, 73, 74, 75, 76, 77], "add_nod": [4, 5, 10, 26, 46, 47, 48, 49, 50, 52], "contain": [4, 5, 7, 8, 17, 20, 23, 24, 41, 46, 47, 48, 49, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 73, 74, 75, 76], "tupl": [4, 5, 8, 20, 23, 24, 31, 41, 46, 47, 48, 50, 51, 52, 53, 54, 55, 60, 64, 65, 73, 76], "anoth": [4, 5, 8, 10, 15, 17, 26, 35, 46, 52], "also": [4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 22, 26, 28, 30, 32, 33, 35, 43, 44, 46, 47, 48, 49, 50, 52, 54, 74, 76], "ad": [4, 5, 6, 7, 8, 9, 23, 24, 30, 31, 33, 41, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 63, 68], "u": [4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 20, 26, 46, 47, 48, 49, 50, 52, 53, 55, 68], "v": [4, 5, 7, 8, 9, 10, 15, 16, 26, 46, 47, 48, 49, 50, 52, 53, 55], "add_edges_from": [4, 5, 21, 23, 39, 46, 47, 48, 49, 50, 52, 53], "connect": [4, 5, 9, 22, 28, 44, 46, 47, 48, 52, 55, 68, 71, 75, 76], "yet": [4, 5, 46, 52], "automat": [4, 5, 9, 14, 24, 46, 48, 52, 53, 55], "There": [4, 5, 6, 8, 9, 12, 14, 16, 17, 46, 48, 52, 68], "error": [4, 5, 8, 10, 17, 20, 31, 46, 47, 48, 49, 50, 52, 55, 75], "alreadi": [4, 5, 7, 9, 10, 46, 48, 52, 53], "exist": [4, 5, 7, 9, 10, 14, 26, 46, 48, 51, 52], "shortcut": [4, 5, 33, 46, 52], "mani": [4, 5, 7, 8, 10, 14, 20, 26, 38, 39, 46, 52, 68], "common": [4, 5, 8, 9, 10, 12, 14, 15, 26, 46, 48, 52], "featur": [4, 5, 6, 7, 8, 9, 10, 17, 26, 28, 30, 46, 52, 53, 68, 77], "allow": [4, 5, 7, 8, 12, 14, 16, 17, 20, 46, 48, 49, 52, 58, 74, 76, 77], "python": [4, 5, 16, 17, 24, 41, 43, 46, 47, 48, 49, 51, 52, 53, 57, 59, 68, 70, 77], "syntax": [4, 5, 17, 46, 48, 52, 55, 68], "speed": [4, 5, 46, 52], "report": [4, 5, 7, 46, 48, 52, 68], "check": [4, 5, 7, 8, 9, 14, 17, 22, 28, 29, 35, 41, 44, 46, 47, 48, 49, 50, 51, 52, 58, 68, 76], "len": [4, 5, 8, 16, 46, 52, 55, 76], "active_trail_nod": [4, 5, 8, 9, 28, 48, 53, 55], "dictionari": [4, 5, 20, 23, 41, 46, 47, 48, 49, 50, 52, 58, 60, 63, 64, 65], "reachabl": [4, 5, 48, 53], "respect": [4, 5, 8, 17, 41, 46, 47, 48, 49, 52, 55], "str": [4, 5, 8, 20, 22, 24, 41, 44, 46, 48, 53, 55, 60, 62, 63, 64, 65, 66, 74, 76, 77], "activ": [4, 5, 8, 9, 10, 26, 48, 53, 55], "trail": [4, 5, 8, 9, 17, 48, 53, 55], "found": [4, 5, 7, 8, 41, 48, 53, 55, 57], "option": [4, 5, 14, 15, 17, 20, 22, 24, 32, 44, 46, 47, 48, 49, 52, 53, 55, 58, 59, 60, 63, 65, 66, 68, 70, 73, 74, 75, 76, 77], "would": [4, 5, 6, 8, 9, 10, 12, 14, 15, 16, 20, 26, 32, 41, 46, 47, 48, 49, 52, 53, 55, 77], "assum": [4, 5, 10, 14, 26, 41, 48, 53, 55, 57, 58, 76], "refer": [4, 5, 10, 17, 20, 21, 23, 24, 26, 28, 32, 39, 41, 42, 46, 48, 52, 54, 55, 74, 75, 76, 77], "detail": [4, 5, 6, 7, 15, 20, 22, 24, 28, 44, 46, 48, 55], "page": [4, 5, 7, 17, 35, 43, 48, 55, 74, 76], "75": [4, 5, 7, 8, 10, 26, 27, 41, 48, 51, 55, 57, 73], "thei": [4, 5, 7, 8, 15, 20, 22, 24, 41, 44, 46, 48, 49, 52, 53, 74, 75, 76], "hashabl": [4, 5, 24, 41, 46, 47, 48, 49, 51, 52, 53, 57, 59, 76, 77], "float": [4, 5, 8, 20, 22, 41, 42, 44, 46, 48, 51, 58, 59, 73, 74, 75, 76], "alic": [4, 5, 47, 52], "bob": [4, 5, 15, 47, 52], "charl": [4, 5, 52], "nodeview": [4, 5, 21, 29, 33, 46, 48, 49], "outedgeview": [4, 5, 16, 29, 46, 48, 53, 74], "present": [4, 5, 14, 16, 23, 46, 47, 48, 49, 52, 53, 58, 59, 62, 65, 66, 73, 74, 75], "ankur": [4, 5, 7, 26], "maria": [4, 5], "behavior": [4, 5, 15, 73, 74, 75], "differ": [4, 5, 7, 8, 9, 10, 14, 16, 17, 22, 26, 28, 31, 32, 41, 44, 48, 55, 74, 76], "than": [4, 5, 6, 7, 8, 9, 14, 15, 17, 23, 41, 48, 58, 74, 76], "index": [4, 5, 8, 15, 31, 41, 43, 48, 63, 64, 68], "associ": [4, 5, 6, 9, 15, 28, 29, 35, 46, 47, 48, 49, 52, 55, 71], "mason": [4, 5], "singl": [4, 5, 7, 8, 15, 17, 22, 33, 47, 48, 50, 57], "sort": [4, 5, 8, 31, 46, 48, 74], "access": [4, 5, 7, 17, 48, 68], "multipl": [4, 5, 6, 12, 46, 47, 48, 51, 76], "behviour": [4, 5], "tell": [4, 5], "d": [4, 5, 6, 7, 9, 10, 12, 15, 16, 20, 22, 23, 24, 26, 28, 38, 39, 41, 44, 46, 48, 53, 55, 57, 58, 59, 65, 68, 74, 75, 76, 77], "e": [4, 5, 7, 8, 9, 10, 15, 16, 17, 20, 22, 23, 24, 26, 35, 38, 39, 41, 42, 44, 46, 48, 52, 53, 58, 59, 65, 74, 75, 76, 77], "inplac": [4, 5, 20, 30, 41, 46, 48, 52, 63, 65, 66], "appli": [4, 5, 6, 9, 14, 20, 31, 46, 48], "oper": [4, 5, 6, 9, 12, 14, 15, 20, 43, 46, 48, 74], "new": [4, 5, 6, 7, 10, 15, 16, 26, 28, 30, 41, 46, 48, 52, 53, 55, 60, 68], "transform": [4, 5, 8, 48, 55], "x": [4, 5, 7, 8, 9, 10, 12, 14, 15, 16, 17, 20, 21, 22, 26, 27, 38, 39, 41, 42, 44, 46, 47, 48, 49, 51, 52, 55, 60, 63, 68, 75, 76], "ha": [4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 22, 23, 24, 26, 28, 30, 32, 33, 35, 37, 41, 44, 48, 51, 55, 65, 74], "effect": [4, 5, 8, 9, 10, 20, 26, 48], "remov": [4, 5, 7, 8, 10, 15, 17, 20, 24, 26, 28, 41, 46, 47, 48, 49, 52, 55, 76], "parent": [4, 5, 6, 7, 8, 9, 10, 14, 20, 26, 41, 46, 48, 52, 53, 55, 58, 59, 62, 63, 65, 66, 73, 74, 75], "make": [4, 5, 6, 7, 8, 9, 12, 15, 17, 20, 46, 48, 49, 55, 68, 75, 76], "chang": [4, 5, 9, 10, 15, 17, 28, 41, 46, 48, 55, 58, 65, 68, 74], "current": [4, 5, 7, 8, 10, 16, 17, 22, 23, 24, 32, 37, 44, 46, 48], "otherwis": [4, 5, 9, 15, 17, 20, 22, 35, 41, 44, 46, 48, 71, 76], "modifi": [4, 5, 7, 9, 15, 20, 28, 37, 41, 46, 48, 71, 76], "y": [4, 5, 7, 10, 14, 15, 16, 20, 21, 22, 26, 27, 34, 41, 44, 46, 48, 51, 55, 60, 75, 76], "desir": [4, 5, 16, 30, 48, 68, 75], "graph_do_a": [4, 5, 48], "we": [4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 22, 23, 24, 26, 28, 30, 31, 32, 33, 35, 37, 38, 41, 44, 46, 47, 48, 49, 52, 68, 75], "verifi": [4, 5, 9, 28, 33, 48, 68], "expect": [4, 5, 8, 17, 33, 43, 48, 56, 76], "causal": [4, 5, 8, 9, 18, 43, 48, 55, 76], "reason": [4, 5, 8, 15, 20, 48], "judea": [4, 5, 10, 20, 26, 48], "pearl": [4, 5, 10, 20, 26, 48], "2000": [4, 5, 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"base-model-structures"]], "Directed Acyclic Graph (DAG)": [[5, "directed-acyclic-graph-dag"], [4, "module-pgmpy.base.DAG"]], "Partial Directed Acyclic Graph (PDAG)": [[5, "partial-directed-acyclic-graph-pdag"]], "Introduction to Probabilitic Graphical Models": [[6, "Introduction-to-Probabilitic-Graphical-Models"]], "Contents": [[6, "Contents"]], "1. What is machine learning": [[6, "1.-What-is-machine-learning"]], "2. Different ways of learning from data": [[6, "2.-Different-ways-of-learning-from-data"]], "Why Probabilistic Graphical Models": [[6, "Why-Probabilistic-Graphical-Models"]], "Types of Graphical Models": [[6, "Types-of-Graphical-Models"]], "Learning Bayesian Networks from Data": [[7, "Learning-Bayesian-Networks-from-Data"]], "Parameter Learning": [[7, "Parameter-Learning"]], "State counts": [[7, "State-counts"]], "Maximum Likelihood Estimation": [[7, "Maximum-Likelihood-Estimation"]], "Bayesian Parameter Estimation": [[7, "Bayesian-Parameter-Estimation"]], "Structure Learning": [[7, "Structure-Learning"], [72, "structure-learning"]], "Scoring functions": [[7, "Scoring-functions"]], "Search strategies": [[7, "Search-strategies"]], "Constraint-based Structure Learning": [[7, "Constraint-based-Structure-Learning"]], "(Conditional) Independence Tests": [[7, "(Conditional)-Independence-Tests"]], "DAG (pattern) construction": [[7, "DAG-(pattern)-construction"]], "Hybrid Structure Learning": [[7, "Hybrid-Structure-Learning"]], "Conclusion": [[7, "Conclusion"]], "References": [[7, "References"]], "A Bayesian Network to model the influence of energy consumption on greenhouse gases in Italy": [[8, "A-Bayesian-Network-to-model-the-influence-of-energy-consumption-on-greenhouse-gases-in-Italy"]], "by Lorenzo Mario Amorosa": [[8, "by-Lorenzo-Mario-Amorosa"]], "Fundamentals of Artificial Intelligence and Knowledge Representation (Mod. 3) - Alma Mater Studiorum Universit\u00e0 di Bologna": [[8, "Fundamentals-of-Artificial-Intelligence-and-Knowledge-Representation-(Mod.-3)---Alma-Mater-Studiorum-Universit\u00e0-di-Bologna"]], "Abstract": [[8, "Abstract"]], "Network definition": [[8, "Network-definition"]], "Datasets": [[8, "Datasets"]], "Raw data": [[8, "Raw-data"]], "Data cleaning": [[8, "Data-cleaning"]], "Data discretization": [[8, "Data-discretization"]], "Learning of network parameters": [[8, "Learning-of-network-parameters"]], "Network analysis": [[8, "Network-analysis"]], "Inferences": [[8, "Inferences"]], "Bayesian Network": [[9, "Bayesian-Network"], [46, "module-pgmpy.models.BayesianNetwork"]], "Bayesian Models": [[9, "Bayesian-Models"]], "1. What are Bayesian Models": [[9, "1.-What-are-Bayesian-Models"]], "2. Independencies in Bayesian Networks": [[9, "2.-Independencies-in-Bayesian-Networks"]], "3. How is this Bayesian Network representing the Joint Distribution over the variables ?": [[9, "3.-How-is-this-Bayesian-Network-representing-the-Joint-Distribution-over-the-variables-?"]], "4. Inference in Bayesian Models": [[9, "4.-Inference-in-Bayesian-Models"]], "Variable Elimination": [[9, "Variable-Elimination"], [12, "Variable-Elimination"], [24, "variable-elimination"]], "Predicting values from new data points": [[9, "Predicting-values-from-new-data-points"]], "5. Other methods for Inference": [[9, "5.-Other-methods-for-Inference"]], "Causal Bayesian Networks": [[10, "Causal-Bayesian-Networks"]], "Objective of the Games": [[10, "Objective-of-the-Games"], [26, "Objective-of-the-Games"]], "Markov Networks": [[11, "Markov-Networks"]], "Markov Models": [[11, "Markov-Models"]], "1. What are Markov Models": [[11, "1.-What-are-Markov-Models"]], "Exact Inference in Graphical Models": [[12, "Exact-Inference-in-Graphical-Models"]], "Inference": [[12, "Inference"]], "Exact Inference": [[12, "Exact-Inference"], [18, "exact-inference"]], "Clique Tree Belief Propagation": [[12, "Clique-Tree-Belief-Propagation"]], "Approximate Inference in Graphical Models": [[13, "Approximate-Inference-in-Graphical-Models"]], "Parameterizing with Continuous Variables": [[14, "Parameterizing-with-Continuous-Variables"]], "Continuous Factors": [[14, "Continuous-Factors"]], "Base Class for Continuous Factors": [[14, "Base-Class-for-Continuous-Factors"]], "Joint Gaussian Distributions": [[14, "Joint-Gaussian-Distributions"]], "Canonical Factors": [[14, "Canonical-Factors"]], "Linear Gaussian CPD": [[14, "Linear-Gaussian-CPD"]], "Reading and Writing from pgmpy file formats": [[16, "Reading-and-Writing-from-pgmpy-file-formats"]], "General WorkFlow of the readwrite module": [[16, "General-WorkFlow-of-the-readwrite-module"]], "Causal Inference": [[20, "causal-inference"]], "Todo": [[20, "id1"]], "Model Testing": [[22, "model-testing"]], "Example Notebooks": [[25, "example-notebooks"]], "Causal Games": [[26, "Causal-Games"]], "Causal Inference Examples": [[27, "Causal-Inference-Examples"]], "Simpson\u2019s paradox": [[27, "Simpson\u2019s-paradox"]], "Model Definition": [[27, "Model-Definition"]], "Inference conditioning on T": [[27, "Inference-conditioning-on-T"]], "Inference with do-operation on T": [[27, "Inference-with-do-operation-on-T"]], "Specifying adjustment sets": [[27, "Specifying-adjustment-sets"]], "Creating discrete Bayesian Networks": [[28, "Creating-discrete-Bayesian-Networks"]], "Step 1: Define the model structure": [[28, "Step-1:-Define-the-model-structure"]], "Step 2: Define the CPDs": [[28, "Step-2:-Define-the-CPDs"]], "Step 3: Add the CPDs to the model.": [[28, "Step-3:-Add-the-CPDs-to-the-model."]], "Step 4: Run basic operations on the model": [[28, "Step-4:-Run-basic-operations-on-the-model"]], "Loading example models": [[28, "Loading-example-models"]], "Example Using the Earthquake network": [[29, "Example-Using-the-Earthquake-network"]], "Extending pgmpy": [[30, "Extending-pgmpy"]], "Linear Gaussian Bayesian Networks (GBNs)": [[31, "Linear-Gaussian-Bayesian-Networks-(GBNs)"]], "Generate x_1 x_2 and Y from a Multivariate Gaussian Distribution with a Mean and a Variance.": [[31, "Generate-x_1-x_2-and-Y-from-a-Multivariate-Gaussian-Distribution-with-a-Mean-and-a-Variance."]], "Linear Gaussian Models - The Process": [[31, "Linear-Gaussian-Models---The-Process"]], "The Condtional Distribution p(Y|X)": [[31, "The-Condtional-Distribution-p(Y|X)"]], "Inference in Discrete Bayesian Network": [[32, "Inference-in-Discrete-Bayesian-Network"]], "Step 1: Define the model.": [[32, "Step-1:-Define-the-model."]], "Step 2: Initialize the inference class": [[32, "Step-2:-Initialize-the-inference-class"]], "Step 3: Doing Inference using hard evidence": [[32, "Step-3:-Doing-Inference-using-hard-evidence"]], "Step 5: Inference using virtual evidence": [[32, "Step-5:-Inference-using-virtual-evidence"]], "Step 4: Troubleshooting for slow inference": [[32, "Step-4:-Troubleshooting-for-slow-inference"]], "Parameter Learning in Discrete Bayesian Networks": [[33, "Parameter-Learning-in-Discrete-Bayesian-Networks"]], "Step 1: Generate some data": [[33, "Step-1:-Generate-some-data"]], "Step 2: Define a model structure": [[33, "Step-2:-Define-a-model-structure"]], "Step 3: Learning the model parameters": [[33, "Step-3:-Learning-the-model-parameters"]], "Monty Hall Problem": [[35, "Monty-Hall-Problem"]], "Problem Description:": [[35, "Problem-Description:"]], "Probabilistic Interpretetion:": [[35, "Probabilistic-Interpretetion:"]], "Normal Bayesian Network (no time variation)": [[36, "Normal-Bayesian-Network-(no-time-variation)"]], "Normal simulation": [[36, "Normal-simulation"]], "Simulation with some evidence": [[36, "Simulation-with-some-evidence"]], "Simulation with soft/virtual evidence": [[36, "Simulation-with-soft/virtual-evidence"]], "Simulation with intervention": [[36, "Simulation-with-intervention"]], "Simulation with soft/virtual intervention": [[36, "Simulation-with-soft/virtual-intervention"]], "Structure Learning in Bayesian Networks": [[37, "Structure-Learning-in-Bayesian-Networks"]], "Generate some data": [[37, "Generate-some-data"]], "Learn the model structure using PC": [[37, "Learn-the-model-structure-using-PC"]], "Learn the model structure using Hill-Climb Search": [[37, "Learn-the-model-structure-using-Hill-Climb-Search"]], "Learning Tree Structure from Data using the Chow-Liu Algorithm": [[38, "Learning-Tree-Structure-from-Data-using-the-Chow-Liu-Algorithm"]], "First, create a tree graph": [[38, "First,-create-a-tree-graph"]], "Then, add CPDs to our tree to create a Bayesian network": [[38, "Then,-add-CPDs-to-our-tree-to-create-a-Bayesian-network"]], "Next, generate sample data from our tree Bayesian network": [[38, "Next,-generate-sample-data-from-our-tree-Bayesian-network"]], "Finally, apply the Chow-Liu algorithm to learn the tree graph from sample data": [[38, "Finally,-apply-the-Chow-Liu-algorithm-to-learn-the-tree-graph-from-sample-data"]], "To parameterize the learned graph from data, check out the other tutorials for more info": [[38, "To-parameterize-the-learned-graph-from-data,-check-out-the-other-tutorials-for-more-info"], [39, "To-parameterize-the-learned-graph-from-data,-check-out-the-other-tutorials-for-more-info"]], "Learning Tree-augmented Naive Bayes (TAN) Structure from Data": [[39, "Learning-Tree-augmented-Naive-Bayes-(TAN)-Structure-from-Data"]], "First, create a Naive Bayes graph": [[39, "First,-create-a-Naive-Bayes-graph"]], "Second, add interaction between the features": [[39, "Second,-add-interaction-between-the-features"]], "Then, parameterize our graph to create a Bayesian network": [[39, "Then,-parameterize-our-graph-to-create-a-Bayesian-network"]], "Next, generate sample data from our Bayesian network": [[39, "Next,-generate-sample-data-from-our-Bayesian-network"]], "Now we are ready to learn the TAN structure from sample data": [[39, "Now-we-are-ready-to-learn-the-TAN-structure-from-sample-data"]], "Discretizing Methods": [[42, "module-pgmpy.factors.continuous.discretize"]], "Supported Data Types": [[43, "supported-data-types"]], "Algorithms": [[43, "algorithms"]], "Indices and tables": [[43, "indices-and-tables"]], "Models": [[45, "models"]], "Cluster Graph": [[47, "module-pgmpy.models.ClusterGraph"]], "Junction Tree": [[50, "module-pgmpy.models.JunctionTree"]], "NoisyOr Model": [[54, "module-pgmpy.models.NoisyOrModel"]], "Parameter Estimation": [[56, "parameter-estimation"]], "Reading/Writing to File": [[61, "reading-writing-to-file"]], "Getting Started": [[67, "getting-started"]], "Contributing to pgmpy": [[68, "contributing-to-pgmpy"]], "Code and Issues": [[68, 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\ No newline at end of file
+Search.setIndex({"docnames": ["approx_infer/approx_infer", "approx_infer/base", "approx_infer/bn_sampling", "approx_infer/gibbs", "base", "base/base", "exact_infer/base", "exact_infer/bp", "exact_infer/causal", "exact_infer/dbn_infer", "exact_infer/model_testing", "exact_infer/mplp", "exact_infer/ve", "examples", "factors/base", "factors/discrete", "factors/discretize", "index", "metrics/metrics", "models/base", "models/bayesiannetwork", "models/clustergraph", "models/dbn", "models/factorgraph", "models/junctiontree", "models/markovchain", "models/markovnetwork", "models/naive", "models/noisyor", "models/sem", "param_estimator/base", "param_estimator/bayesian_est", "param_estimator/em", "param_estimator/mle", "param_estimator/sem_estimator", "readwrite/base", "readwrite/bif", "readwrite/pomdpx", "readwrite/uai", "readwrite/xmlbelief", "readwrite/xmlbif", "started/base", "started/contributing", "started/getting_started", "started/install", "started/license", "structure_estimator/base", 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"param_estimator/sem_estimator.rst", "readwrite/base.rst", "readwrite/bif.rst", "readwrite/pomdpx.rst", "readwrite/uai.rst", "readwrite/xmlbelief.rst", "readwrite/xmlbif.rst", "started/base.rst", "started/contributing.rst", "started/getting_started.rst", "started/install.rst", "started/license.rst", "structure_estimator/base.rst", "structure_estimator/exhaustive.rst", "structure_estimator/hill.rst", "structure_estimator/mmhc.rst", "structure_estimator/pc.rst", "structure_estimator/tree.rst", "tutorial.rst"], "titles": ["Approximate Inference Using Sampling", "Approximate Inference", "Bayesian Model Sampling", "Gibbs Sampling", "Base Structure Classes", "Base Model Structures", "Exact Inference", "Belief Propagation", "Causal Inference", "Dynamic Bayesian Network Inference", "Model Testing", "MPLP", "Variable Elimination", "Example Notebooks", "Parameterization", "Discrete", "Discretizing Methods", "Supported Data Types", "Metrics for testing models", "Models", "Bayesian Network", 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40, 47, 48, 49, 50, 51], "margin": [0, 12, 15, 20, 23, 48], "queri": [0, 2, 7, 8, 9, 11, 12, 20, 22], "n_sampl": [0, 10, 20, 22, 29, 50], "10000": 0, "evid": [0, 2, 3, 7, 8, 9, 12, 15, 20, 22], "virtual_evid": [0, 7, 12, 20, 22], "show_progress": [0, 2, 7, 8, 12, 20, 22, 32, 48, 50, 51], "seed": [0, 2, 3, 15, 20, 22, 25, 32], "method": [0, 2, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 23, 24, 25, 26, 29, 31, 32, 33, 34, 36, 37, 47, 48, 49, 50, 51], "do": [0, 4, 5, 8, 17, 20, 22, 42, 45, 50], "base": [0, 8, 10, 12, 15, 16, 17, 18, 20, 21, 22, 26, 28, 29, 34, 46, 47, 48, 49, 51], "bayesian": [0, 1, 4, 5, 6, 8, 10, 15, 17, 18, 19, 26, 27, 30, 35, 37, 38, 39, 40, 48, 49, 50, 51], "network": [0, 2, 4, 5, 6, 8, 10, 12, 15, 17, 18, 19, 27, 32, 33, 36, 37, 38, 39, 40, 47, 48, 49, 50, 51], "dynam": [0, 6, 19], "which": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 25, 26, 29, 31, 33, 34, 36, 37, 38, 39, 40, 47, 48, 50], "probabl": [0, 3, 4, 5, 7, 8, 9, 10, 11, 12, 16, 20, 22, 25, 28, 36, 37, 38, 48, 49, 50, 51], "calcul": [0, 10, 15, 16], "int": [0, 2, 3, 4, 5, 10, 15, 16, 18, 20, 21, 22, 23, 25, 26, 29, 31, 32, 33, 36, 38, 40, 48, 49, 50, 51], "The": [0, 2, 3, 4, 5, 8, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 24, 26, 27, 29, 31, 32, 33, 34, 36, 37, 38, 39, 40, 45, 47, 48, 49, 50, 51], "number": [0, 2, 3, 4, 5, 11, 12, 15, 16, 20, 21, 22, 23, 25, 26, 29, 31, 32, 33, 34, 36, 38, 47, 48, 50, 51], "higher": [0, 10, 18, 50], "result": [0, 11, 20, 47, 48, 49, 50], "more": [0, 10, 18, 20, 22, 31, 50], "accur": 0, "cost": [0, 12], "time": [0, 4, 5, 11, 17, 20, 22, 23, 24, 26], "pd": [0, 8, 10, 12, 18, 20, 22, 27, 29, 31, 32, 33, 34, 47, 48, 49, 50, 51], "provid": [0, 2, 3, 4, 5, 11, 12, 15, 20, 22, 29, 31, 37, 45, 47, 48, 49, 50], "instead": [0, 2, 20, 22, 28, 31, 42, 50], "must": [0, 4, 5, 15, 16, 20, 21, 22, 25, 29, 31, 33, 50], "conform": 0, "observ": [0, 4, 5, 8, 20, 22, 25, 27, 29, 32, 33, 34, 37, 47, 48, 49, 50], "valu": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 38, 39, 47, 48, 49, 50, 51], "kei": [0, 4, 5, 7, 9, 12, 20, 21, 22, 23, 26, 29, 34, 38], "pair": [0, 7, 8, 9, 10, 11, 12, 18, 21, 22, 47, 49, 50], "var": [0, 2, 3, 7, 9, 12, 15, 22, 25, 29, 34, 37], "factor": [0, 2, 3, 4, 5, 7, 8, 9, 11, 12, 16, 19, 20, 21, 22, 24, 25, 26, 33, 37], "discret": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 17, 18, 20, 21, 22, 23, 24, 25, 26, 32, 33, 39, 47, 48, 49, 50, 51], "tabularcpd": [0, 2, 3, 4, 5, 7, 9, 12, 20, 22, 27, 31, 32, 33], "repres": [0, 3, 4, 5, 7, 8, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 38, 47, 48, 49, 50, 51], "virtual": [0, 7, 12, 20, 22, 31], "soft": [0, 20, 22], "show": [0, 2, 7, 12, 15, 20, 22, 32, 51], "progress": [0, 2, 7, 12, 20, 22, 32, 51], "bar": [0, 2, 7, 12, 20, 22, 32, 51], "when": [0, 4, 5, 11, 15, 20, 22, 26, 29, 32, 42, 48, 50], "set": [0, 2, 3, 4, 5, 8, 10, 11, 12, 15, 17, 20, 21, 22, 23, 24, 25, 26, 29, 31, 32, 33, 38, 39, 47, 48, 49, 50, 51], "random": [0, 2, 3, 4, 5, 8, 11, 12, 15, 16, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 47, 48, 49, 50, 51], "type": [0, 2, 3, 4, 5, 8, 10, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 38, 39, 40, 47, 48, 49, 50, 51], "exampl": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39, 40, 42, 47, 48, 49, 50, 51], "util": [0, 4, 5, 8, 10, 15, 18, 20, 22, 36, 38, 40, 50], "import": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 38, 39, 40, 47, 48, 49, 50, 51], "get_example_model": [0, 4, 5, 8, 10, 15, 18, 20, 22, 36, 38, 40, 50], "alarm": [0, 4, 5, 8, 10, 15, 18, 20, 22, 50], "histori": [0, 8], "discretefactor": [0, 3, 8, 11, 15, 16, 21, 23, 24, 26], "phi": [0, 8, 11, 15, 21, 23, 24, 26, 29], "2": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 47, 48, 49, 50, 51], "0x7f92d9f5b910": 0, "cvp": [0, 8, 20], "3": [0, 2, 3, 4, 5, 7, 9, 10, 11, 12, 15, 16, 18, 20, 21, 22, 25, 26, 28, 29, 32, 36, 37, 38, 39, 40, 44, 47, 48, 49, 50, 51], "0x7f92d9f77610": 0, "fals": [0, 2, 3, 4, 5, 7, 8, 10, 11, 12, 15, 18, 20, 22, 23, 25, 26, 31, 33, 36, 37, 40, 49, 50], "0x7f92dc61eb50": 0, "0x7f92d915ec40": 0, "sampl": [1, 15, 17, 20, 22, 25, 29, 47, 48], "us": [1, 2, 4, 5, 7, 8, 9, 10, 11, 15, 16, 18, 20, 22, 26, 27, 28, 29, 31, 32, 33, 34, 36, 42, 44, 45, 47, 48, 49, 50, 51], "gibb": 1, "bayesianmodelsampl": [2, 20], "specif": [2, 8, 10, 22, 29], "instanc": [0, 2, 4, 5, 8, 10, 12, 15, 18, 20, 22, 27, 29, 32, 33, 34, 36, 37, 38, 39, 40, 47, 48, 49, 50], "bayesiannetwork": [0, 2, 3, 7, 8, 10, 12, 15, 18, 20, 31, 32, 33, 36, 39, 40, 48, 49], "infer": [2, 4, 5, 7, 11, 12, 17, 22, 29], "forward_sampl": [2, 20], "size": [2, 3, 8, 12, 20, 22, 25, 26, 27, 31, 32, 33, 37, 47, 48, 49, 50, 51], "1": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39, 40, 42, 47, 48, 49, 50, 51], "include_lat": [2, 3, 4, 5, 20, 22], "partial_sampl": [2, 20], "n_job": [2, 20, 31, 32, 33, 36, 50, 51], "": [2, 4, 5, 10, 12, 15, 16, 18, 20, 22, 26, 29, 32, 33, 34, 37, 42, 47, 48, 49, 50], "whether": [2, 3, 4, 5, 8, 10, 11, 15, 18, 20, 22, 23, 32, 45, 50], "includ": [2, 3, 4, 5, 8, 20, 22, 45], "latent": [2, 3, 4, 5, 20, 22, 26, 29, 32, 34], "numpi": [2, 3, 11, 12, 15, 16, 20, 22, 23, 24, 27, 31, 32, 33, 44, 47, 48, 49, 50, 51], "get": [2, 15, 22, 36], "specifi": [2, 4, 5, 8, 10, 15, 18, 20, 21, 22, 23, 26, 27, 29, 31, 32, 33, 34, 50], "procedur": [2, 20, 48, 49, 50], "them": [2, 8, 20, 23], "cpu": [2, 20], "core": [2, 20, 50], "all": [2, 3, 4, 5, 8, 11, 12, 15, 16, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 38, 42, 45, 47, 48, 49, 50, 51], "student": [2, 3, 4, 5, 11, 20, 21, 22, 26], "diff": [2, 4, 5, 7, 15, 20, 22, 25, 42], "grade": [2, 4, 5, 7, 15, 20, 22, 25], "intel": [2, 3, 4, 5, 7, 15, 20, 22, 25], "cpd_d": [2, 12], "0": [2, 3, 4, 5, 7, 9, 10, 11, 12, 15, 16, 18, 20, 22, 25, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39, 40, 42, 47, 48, 49, 50, 51], "6": [2, 3, 4, 5, 7, 9, 10, 12, 15, 16, 18, 20, 21, 22, 25, 26, 28, 36, 39, 40, 47, 48], "4": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 25, 26, 28, 31, 33, 36, 38, 39, 40, 48, 49, 50], "cpd_i": [2, 12], "7": [2, 3, 4, 5, 7, 9, 10, 15, 18, 20, 22, 25, 28, 29, 36, 39, 40, 44], "cpd_g": [2, 7, 12], "05": [2, 3, 10, 15, 16, 18, 22, 36, 39, 40, 50], "9": [2, 4, 5, 7, 9, 15, 16, 20, 22, 36, 39, 40, 48], "5": [2, 3, 4, 5, 7, 9, 10, 11, 12, 15, 16, 18, 20, 22, 25, 27, 31, 32, 33, 42, 47, 48, 49, 50, 51], "25": [2, 15, 22, 25, 31], "08": [2, 15, 20, 22, 32], "02": [2, 22], "add_cpd": [2, 3, 7, 9, 12, 15, 20, 22], "rec": 2, "dtype": [2, 20, 32, 33, 47, 48, 49, 50], "i8": 2, "likelihood_weighted_sampl": 2, "weight": [2, 4, 5, 8, 12, 20, 22, 29, 31, 32, 33, 34, 51], "compli": 2, "probabilist": [2, 4, 5, 7, 17, 20, 22, 29, 48, 50], "graphic": [2, 4, 5, 7, 8, 22, 29, 48, 50], "principl": [2, 4, 5, 22, 29, 48, 50], "techniqu": [2, 4, 5, 22, 29, 42, 48, 50], "koller": [2, 4, 5, 22, 29, 48, 50], "friedman": [2, 4, 5, 22, 29, 48, 50, 51], "algorithm": [2, 4, 5, 8, 9, 11, 12, 16, 22, 23, 26, 29, 32, 48, 49, 51], "12": [2, 15, 16], "pp": [2, 48], "493": 2, "namedtupl": [2, 3, 25], "correspond": [2, 10, 15, 20, 21, 22, 23, 26, 28], "return_typ": [2, 50], "recarrai": 2, "_weight": [2, 31, 33], "f8": 2, "rejection_sampl": 2, "gibbssampl": 3, "perform": [3, 7, 8, 9, 11, 48], "markovnetwork": [3, 7, 11, 26], "ar": [3, 4, 5, 8, 10, 11, 12, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 38, 42, 47, 48, 49, 50, 51], "inherit": [3, 12, 34], "transit": [3, 25, 37], "initi": [0, 3, 4, 5, 11, 15, 20, 21, 22, 23, 24, 25, 26, 29, 34, 36, 37, 38, 39], "object": [3, 4, 5, 8, 11, 12, 15, 16, 20, 21, 22, 23, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 40, 47, 48, 49, 50, 51], "intel_cpd": [3, 7, 15, 20, 22], "sat_cpd": [3, 7, 20], "sat": [3, 4, 5, 7, 20, 22], "95": [3, 20, 36, 37, 39, 40], "8": [3, 4, 5, 7, 9, 15, 16, 20, 21, 22, 29, 37, 39, 40], "evidence_card": [3, 7, 9, 15, 20, 22], "add_nodes_from": [3, 4, 5, 11, 20, 21, 22, 23, 24, 26, 27], "add_edg": [3, 4, 5, 20, 21, 22, 23, 24, 26, 27], "gibbs_chain": 3, "generate_sampl": [3, 25, 29], "start_stat": [3, 25], "version": [3, 22, 25, 39, 42, 50], "self": [3, 4, 5, 8, 15, 16, 20, 22, 25], "assign": [3, 11, 15, 22, 25, 31, 33], "b": [3, 4, 5, 8, 11, 12, 15, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 39, 42, 47, 48, 49, 50, 51], "c": [3, 4, 5, 9, 11, 12, 15, 20, 21, 22, 23, 24, 26, 27, 31, 32, 33, 36, 39, 40, 44, 45, 47, 48, 49, 50, 51], "factor_ab": 3, "factor_cb": 3, "add_factor": [3, 11, 21, 23, 24, 26], "gen": [3, 25], "markov": [3, 4, 5, 8, 19, 20, 22, 23, 37, 38], "chain": [3, 4, 5, 19, 22], "iter": [3, 4, 5, 11, 20, 21, 22, 25, 32, 34, 48], "start": [3, 4, 5, 15, 22, 25, 27, 29, 34, 42, 48], "pass": [3, 4, 5, 8, 10, 11, 16, 18, 20, 21, 22, 24, 25, 26, 29, 50], "chosen": [3, 25, 29], "return_tup": 3, "ebunch": [4, 5, 20, 21, 22, 23, 24, 26, 27, 29], "node": [4, 5, 8, 9, 11, 12, 16, 20, 21, 22, 23, 24, 26, 27, 29, 31, 33, 47, 48, 49, 50, 51], "can": [4, 5, 10, 11, 15, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 42, 47, 48, 49, 50], "either": [4, 5, 8, 15, 20, 22, 29, 31, 34, 48, 50], "cluster": [4, 5, 11, 19], "edg": [4, 5, 8, 11, 12, 15, 20, 21, 22, 23, 24, 26, 27, 29, 36, 38, 39, 40, 47, 48, 49, 50, 51], "depend": [4, 5, 26, 27, 44, 48, 49, 50, 51], "between": [4, 5, 8, 10, 11, 15, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 48], "input": [4, 5, 7, 9, 20, 21, 22, 23, 24, 26], "an": [4, 5, 8, 11, 12, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 45, 47, 48, 49, 50, 51], "empti": [4, 5, 20, 21, 22, 23, 24, 25, 26, 47], "creat": [4, 5, 7, 8, 20, 21, 22, 23, 24, 25, 26, 29, 36, 42, 47, 48], "ani": [4, 5, 8, 10, 12, 15, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 33, 42, 45, 48, 49, 50, 51], "networkx": [4, 5, 20, 22, 26, 47, 50, 51], "g": [4, 5, 7, 8, 12, 15, 16, 20, 21, 22, 23, 24, 26, 27, 48, 50], "grown": [4, 5, 20, 21, 23, 24, 26], "sever": [4, 5, 20, 25, 26], "wai": [4, 5, 20, 22, 26, 39, 42, 50], "add": [4, 5, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 36, 37, 38, 40, 42, 48], "one": [4, 5, 10, 18, 20, 22, 23, 24, 26, 27, 29, 31, 33, 47, 48, 49, 50, 51], "add_nod": [4, 5, 20, 21, 22, 23, 24, 26], "contain": [4, 5, 8, 11, 12, 15, 20, 21, 22, 23, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39, 47, 48, 49, 50], "tupl": [4, 5, 8, 11, 12, 15, 20, 21, 22, 24, 25, 26, 27, 28, 29, 34, 38, 39, 47, 50], "anoth": [4, 5, 20, 26], "also": [4, 5, 10, 17, 18, 20, 21, 22, 23, 24, 26, 28, 48, 50], "ad": [4, 5, 11, 12, 15, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 37, 42], "u": [4, 5, 20, 21, 22, 23, 24, 26, 27, 29, 42], "v": [4, 5, 20, 21, 22, 23, 24, 26, 27, 29], "add_edges_from": [4, 5, 9, 11, 20, 21, 22, 23, 24, 26, 27], "connect": [4, 5, 10, 18, 20, 21, 22, 26, 29, 42, 45, 49, 50], "yet": [4, 5, 20, 26], "automat": [4, 5, 12, 20, 22, 26, 27, 29], "There": [4, 5, 20, 22, 26, 42], "error": [4, 5, 8, 20, 21, 22, 23, 24, 26, 29, 49], "alreadi": [4, 5, 20, 22, 26, 27], "exist": [4, 5, 20, 22, 25, 26], "shortcut": [4, 5, 20, 26], "mani": [4, 5, 20, 26, 42], "common": [4, 5, 20, 22, 26], "featur": [4, 5, 20, 26, 27, 42, 51], "allow": [4, 5, 20, 22, 23, 26, 32, 48, 50, 51], "python": [4, 5, 12, 15, 17, 20, 21, 22, 23, 25, 26, 27, 31, 33, 42, 44, 51], "syntax": [4, 5, 20, 22, 26, 29, 42], "speed": [4, 5, 20, 26], "report": [4, 5, 20, 22, 26, 42], "check": [4, 5, 10, 15, 18, 20, 21, 22, 23, 24, 25, 26, 32, 42, 50], "len": [4, 5, 20, 26, 29, 50], "active_trail_nod": [4, 5, 22, 27, 29], "dictionari": [4, 5, 8, 11, 15, 20, 21, 22, 23, 24, 26, 32, 34, 37, 38, 39], "reachabl": [4, 5, 22, 27], "respect": [4, 5, 15, 20, 21, 22, 23, 26, 29], "str": [4, 5, 8, 10, 12, 15, 18, 20, 22, 27, 29, 34, 36, 37, 38, 39, 40, 48, 50, 51], "activ": [4, 5, 22, 27, 29], "trail": [4, 5, 22, 27, 29], "found": [4, 5, 15, 22, 27, 29, 31], "option": [4, 5, 8, 10, 12, 18, 20, 21, 22, 23, 26, 27, 29, 32, 33, 34, 37, 39, 40, 42, 44, 47, 48, 49, 50, 51], "would": [4, 5, 15, 20, 21, 22, 23, 26, 27, 29, 51], "assum": [4, 5, 15, 22, 27, 29, 31, 32, 50], "refer": [4, 5, 8, 9, 11, 12, 15, 16, 20, 22, 26, 28, 29, 31, 48, 49, 50, 51], "detail": [4, 5, 8, 10, 12, 18, 20, 22, 29, 31], "page": [4, 5, 17, 22, 29, 48, 50], "75": [4, 5, 15, 22, 25, 29, 31, 47], "thei": [4, 5, 8, 10, 12, 15, 18, 20, 22, 23, 26, 27, 48, 49, 50], "hashabl": [4, 5, 12, 15, 20, 21, 22, 23, 25, 26, 27, 31, 33, 50, 51], "float": [4, 5, 8, 10, 15, 16, 18, 20, 22, 25, 32, 33, 47, 48, 49, 50], "alic": [4, 5, 21, 26], "bob": [4, 5, 21, 26], "charl": [4, 5, 26], "nodeview": [4, 5, 9, 20, 22, 23], "outedgeview": [4, 5, 20, 22, 27, 48], "present": [4, 5, 11, 21, 22, 23, 26, 27, 36, 39, 40], "ankur": [4, 5], "maria": [4, 5], "behavior": [4, 5], "differ": [4, 5, 10, 15, 18, 22, 29, 48, 50], "than": [4, 5, 11, 15, 22, 32, 48, 50], "index": [4, 5, 15, 17, 22, 37, 38, 42], "associ": [4, 5, 20, 21, 22, 23, 26, 29, 45], "mason": [4, 5], "singl": [4, 5, 10, 21, 22, 24, 31], "sort": [4, 5, 20, 22, 48], "access": [4, 5, 22, 42], "multipl": [4, 5, 20, 21, 22, 25, 50], "behviour": [4, 5], "tell": [4, 5], "d": [4, 5, 8, 10, 11, 12, 15, 18, 20, 22, 27, 29, 31, 32, 33, 39, 42, 48, 49, 50, 51], "e": [4, 5, 8, 10, 11, 12, 15, 16, 18, 20, 22, 26, 27, 32, 33, 39, 48, 49, 50, 51], "inplac": [4, 5, 8, 15, 20, 22, 26, 37, 39, 40], "appli": [4, 5, 8, 20, 22], "oper": [4, 5, 8, 17, 20, 22, 48], "new": [4, 5, 15, 20, 22, 26, 27, 29, 34, 42], "transform": [4, 5, 22, 29], "x": [4, 5, 8, 9, 10, 15, 16, 18, 20, 21, 22, 23, 25, 26, 29, 34, 37, 42, 49, 50], "ha": [4, 5, 10, 11, 12, 15, 18, 22, 25, 29, 39, 48], "effect": [4, 5, 8, 22], "remov": [4, 5, 8, 12, 15, 20, 21, 22, 23, 26, 29, 50], "parent": [4, 5, 8, 15, 20, 22, 26, 27, 29, 36, 37, 39, 40, 48, 49], "make": [4, 5, 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(pgmpy.readwrite.uai.uaireader method)": [[38, "pgmpy.readwrite.UAI.UAIReader.get_grammar"]], "get_model() (pgmpy.readwrite.uai.uaireader method)": [[38, "pgmpy.readwrite.UAI.UAIReader.get_model"]], "get_network_type() (pgmpy.readwrite.uai.uaireader method)": [[38, "pgmpy.readwrite.UAI.UAIReader.get_network_type"]], "get_nodes() (pgmpy.readwrite.uai.uaiwriter method)": [[38, "pgmpy.readwrite.UAI.UAIWriter.get_nodes"]], "get_tables() (pgmpy.readwrite.uai.uaireader method)": [[38, "pgmpy.readwrite.UAI.UAIReader.get_tables"]], "get_tables() (pgmpy.readwrite.uai.uaiwriter method)": [[38, "pgmpy.readwrite.UAI.UAIWriter.get_tables"]], "get_variables() (pgmpy.readwrite.uai.uaireader method)": [[38, "pgmpy.readwrite.UAI.UAIReader.get_variables"]], "pgmpy.readwrite.uai": [[38, "module-pgmpy.readwrite.UAI"]], "write_uai() (pgmpy.readwrite.uai.uaiwriter method)": [[38, "pgmpy.readwrite.UAI.UAIWriter.write_uai"]], "xbnreader (class in pgmpy.readwrite.xmlbeliefnetwork)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNReader"]], "xbnwriter (class in pgmpy.readwrite.xmlbeliefnetwork)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNWriter"]], "get_analysisnotebook_values() (pgmpy.readwrite.xmlbeliefnetwork.xbnreader method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNReader.get_analysisnotebook_values"]], "get_bnmodel_name() (pgmpy.readwrite.xmlbeliefnetwork.xbnreader method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNReader.get_bnmodel_name"]], "get_distributions() (pgmpy.readwrite.xmlbeliefnetwork.xbnreader method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNReader.get_distributions"]], "get_edges() (pgmpy.readwrite.xmlbeliefnetwork.xbnreader method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNReader.get_edges"]], "get_model() (pgmpy.readwrite.xmlbeliefnetwork.xbnreader method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNReader.get_model"]], "get_static_properties() (pgmpy.readwrite.xmlbeliefnetwork.xbnreader method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNReader.get_static_properties"]], "get_variables() (pgmpy.readwrite.xmlbeliefnetwork.xbnreader method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNReader.get_variables"]], "indent() (pgmpy.readwrite.xmlbeliefnetwork.xbnwriter method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNWriter.indent"]], "pgmpy.readwrite.xmlbeliefnetwork": [[39, "module-pgmpy.readwrite.XMLBeliefNetwork"]], "set_analysisnotebook() (pgmpy.readwrite.xmlbeliefnetwork.xbnwriter method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNWriter.set_analysisnotebook"]], "set_bnmodel_name() (pgmpy.readwrite.xmlbeliefnetwork.xbnwriter method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNWriter.set_bnmodel_name"]], "set_distributions() (pgmpy.readwrite.xmlbeliefnetwork.xbnwriter method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNWriter.set_distributions"]], "set_edges() (pgmpy.readwrite.xmlbeliefnetwork.xbnwriter method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNWriter.set_edges"]], "set_static_properties() (pgmpy.readwrite.xmlbeliefnetwork.xbnwriter method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNWriter.set_static_properties"]], "set_variables() (pgmpy.readwrite.xmlbeliefnetwork.xbnwriter method)": [[39, "pgmpy.readwrite.XMLBeliefNetwork.XBNWriter.set_variables"]], "xmlbifreader (class in pgmpy.readwrite.xmlbif)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFReader"]], "xmlbifwriter (class in pgmpy.readwrite.xmlbif)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFWriter"]], "get_definition() (pgmpy.readwrite.xmlbif.xmlbifwriter method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFWriter.get_definition"]], "get_edges() (pgmpy.readwrite.xmlbif.xmlbifreader method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFReader.get_edges"]], "get_model() (pgmpy.readwrite.xmlbif.xmlbifreader method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFReader.get_model"]], "get_parents() (pgmpy.readwrite.xmlbif.xmlbifreader method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFReader.get_parents"]], "get_properties() (pgmpy.readwrite.xmlbif.xmlbifwriter method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFWriter.get_properties"]], "get_property() (pgmpy.readwrite.xmlbif.xmlbifreader method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFReader.get_property"]], "get_states() (pgmpy.readwrite.xmlbif.xmlbifreader method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFReader.get_states"]], "get_states() (pgmpy.readwrite.xmlbif.xmlbifwriter method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFWriter.get_states"]], "get_values() (pgmpy.readwrite.xmlbif.xmlbifreader method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFReader.get_values"]], "get_values() (pgmpy.readwrite.xmlbif.xmlbifwriter method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFWriter.get_values"]], "get_variables() (pgmpy.readwrite.xmlbif.xmlbifreader method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFReader.get_variables"]], "get_variables() (pgmpy.readwrite.xmlbif.xmlbifwriter method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFWriter.get_variables"]], "indent() (pgmpy.readwrite.xmlbif.xmlbifwriter method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFWriter.indent"]], "pgmpy.readwrite.xmlbif": [[40, "module-pgmpy.readwrite.XMLBIF"]], "write_xmlbif() (pgmpy.readwrite.xmlbif.xmlbifwriter method)": [[40, "pgmpy.readwrite.XMLBIF.XMLBIFWriter.write_xmlbif"]], "exhaustivesearch (class in pgmpy.estimators)": [[47, "pgmpy.estimators.ExhaustiveSearch"]], "all_dags() (pgmpy.estimators.exhaustivesearch method)": [[47, "pgmpy.estimators.ExhaustiveSearch.all_dags"]], "all_scores() (pgmpy.estimators.exhaustivesearch method)": [[47, "pgmpy.estimators.ExhaustiveSearch.all_scores"]], "estimate() (pgmpy.estimators.exhaustivesearch method)": [[47, "pgmpy.estimators.ExhaustiveSearch.estimate"]], "bdeuscore (class in pgmpy.estimators)": [[48, "pgmpy.estimators.BDeuScore"]], "bdsscore (class in pgmpy.estimators)": [[48, "pgmpy.estimators.BDsScore"]], "bicscore (class in pgmpy.estimators)": [[48, "pgmpy.estimators.BicScore"]], "hillclimbsearch (class in pgmpy.estimators)": [[48, "pgmpy.estimators.HillClimbSearch"]], "k2score (class in pgmpy.estimators)": [[48, "pgmpy.estimators.K2Score"]], "estimate() (pgmpy.estimators.hillclimbsearch method)": [[48, "pgmpy.estimators.HillClimbSearch.estimate"]], "local_score() (pgmpy.estimators.bdeuscore method)": [[48, "pgmpy.estimators.BDeuScore.local_score"]], "local_score() (pgmpy.estimators.bdsscore method)": [[48, "pgmpy.estimators.BDsScore.local_score"]], "local_score() (pgmpy.estimators.bicscore method)": [[48, "pgmpy.estimators.BicScore.local_score"]], "local_score() (pgmpy.estimators.k2score method)": [[48, "pgmpy.estimators.K2Score.local_score"]], "structure_prior() (pgmpy.estimators.bdsscore method)": [[48, "pgmpy.estimators.BDsScore.structure_prior"]], "structure_prior_ratio() (pgmpy.estimators.bdsscore method)": [[48, "pgmpy.estimators.BDsScore.structure_prior_ratio"]], "mmhcestimator (class in pgmpy.estimators)": [[49, "pgmpy.estimators.MmhcEstimator"]], "estimate() (pgmpy.estimators.mmhcestimator method)": [[49, "pgmpy.estimators.MmhcEstimator.estimate"]], "mmpc() (pgmpy.estimators.mmhcestimator method)": [[49, "pgmpy.estimators.MmhcEstimator.mmpc"]], "pc (class in pgmpy.estimators)": [[50, "pgmpy.estimators.PC"]], "build_skeleton() (pgmpy.estimators.pc method)": [[50, "pgmpy.estimators.PC.build_skeleton"]], "chi_square() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.chi_square"]], "cressie_read() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.cressie_read"]], "estimate() (pgmpy.estimators.pc method)": [[50, "pgmpy.estimators.PC.estimate"]], "freeman_tuckey() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.freeman_tuckey"]], "g_sq() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.g_sq"]], "independence_match() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.independence_match"]], "log_likelihood() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.log_likelihood"]], "modified_log_likelihood() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.modified_log_likelihood"]], "neyman() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.neyman"]], "pearsonr() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.pearsonr"]], "pgmpy.estimators.citests": [[50, "module-pgmpy.estimators.CITests"]], "power_divergence() (in module pgmpy.estimators.citests)": [[50, "pgmpy.estimators.CITests.power_divergence"]], "skeleton_to_pdag() (pgmpy.estimators.pc static method)": [[50, "pgmpy.estimators.PC.skeleton_to_pdag"]], "treesearch (class in pgmpy.estimators)": [[51, "pgmpy.estimators.TreeSearch"]], "estimate() (pgmpy.estimators.treesearch method)": [[51, "pgmpy.estimators.TreeSearch.estimate"]]}})
\ No newline at end of file
diff --git a/structure_estimator/exhaustive.html b/structure_estimator/exhaustive.html
index da30899..97524ea 100644
--- a/structure_estimator/exhaustive.html
+++ b/structure_estimator/exhaustive.html
@@ -54,10 +54,6 @@ <h1>Exhaustive Search<a class="headerlink" href="#exhaustive-search" title="Perm
 <li><p><strong>use_caching</strong> (<em>boolean</em>) – If True, uses caching of score for faster computation.
 Note: Caching only works for scoring methods which are decomposable. Can
 give wrong results in case of custom scoring methods.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.Nan</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.
-This sets the behavior of the <cite>state_count</cite>-method.</p></li>
 </ul>
 </dd>
 </dl>
@@ -161,7 +157,7 @@ <h1>Exhaustive Search<a class="headerlink" href="#exhaustive-search" title="Perm
 <dd class="field-odd"><p><strong>Estimated Model</strong> – A <cite>DAG</cite> with maximal score.</p>
 </dd>
 <dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>pgmpy.base.DAG</p>
+<dd class="field-even"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
diff --git a/structure_estimator/hill.html b/structure_estimator/hill.html
index 7ab8512..37ab4f9 100644
--- a/structure_estimator/hill.html
+++ b/structure_estimator/hill.html
@@ -49,10 +49,6 @@ <h1>Hill Climb Search<a class="headerlink" href="#hill-climb-search" title="Perm
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states (or values)
 that the variable can take. If unspecified, the observed values in the data set
 are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.Nan</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.
-This sets the behavior of the <cite>state_count</cite>-method.</p></li>
 <li><p><strong>use_caching</strong> (<em>boolean</em>) – If True, uses caching of score for faster computation.
 Note: Caching only works for scoring methods which are decomposable. Can
 give wrong results in case of custom scoring methods.</p></li>
@@ -101,7 +97,7 @@ <h1>Hill Climb Search<a class="headerlink" href="#hill-climb-search" title="Perm
 <dd class="field-even"><p><strong>Estimated model</strong> – A <cite>DAG</cite> at a (local) score maximum.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
@@ -150,10 +146,6 @@ <h2>BDeu Score<a class="headerlink" href="#bdeu-score" title="Permalink to this
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states (or values)
 that the variable can take. If unspecified, the observed values in the data set
 are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.Nan</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.
-This sets the behavior of the <cite>state_count</cite>-method.</p></li>
 </ul>
 </dd>
 </dl>
@@ -191,10 +183,6 @@ <h2>Bic Score<a class="headerlink" href="#bic-score" title="Permalink to this he
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states (or values)
 that the variable can take. If unspecified, the observed values in the data set
 are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.Nan</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.
-This sets the behavior of the <cite>state_count</cite>-method.</p></li>
 </ul>
 </dd>
 </dl>
@@ -229,10 +217,6 @@ <h2>K2 Score<a class="headerlink" href="#k2-score" title="Permalink to this head
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states (or values)
 that the variable can take. If unspecified, the observed values in the data set
 are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.Nan</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.
-This sets the behavior of the <cite>state_count</cite>-method.</p></li>
 </ul>
 </dd>
 </dl>
@@ -275,10 +259,6 @@ <h2>BDsScore<a class="headerlink" href="#bdsscore" title="Permalink to this head
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states (or values)
 that the variable can take. If unspecified, the observed values in the data set
 are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.Nan</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.
-This sets the behavior of the <cite>state_count</cite>-method.</p></li>
 </ul>
 </dd>
 </dl>
diff --git a/structure_estimator/mmhc.html b/structure_estimator/mmhc.html
index 8f877a3..9bc69fb 100644
--- a/structure_estimator/mmhc.html
+++ b/structure_estimator/mmhc.html
@@ -49,10 +49,6 @@ <h1>Mmhc Estimator<a class="headerlink" href="#mmhc-estimator" title="Permalink
 <li><p><strong>state_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> (</em><em>optional</em><em>)</em>) – A dict indicating, for each variable, the discrete set of states (or values)
 that the variable can take. If unspecified, the observed values in the data set
 are taken to be the only possible states.</p></li>
-<li><p><strong>complete_samples_only</strong> (bool (optional, default <cite>True</cite>)) – Specifies how to deal with missing data, if present. If set to <cite>True</cite> all rows
-that contain <cite>np.Nan</cite> somewhere are ignored. If <cite>False</cite> then, for each variable,
-every row where neither the variable nor its parents are <cite>np.NaN</cite> is used.
-This sets the behavior of the <cite>state_count</cite>-method.</p></li>
 </ul>
 </dd>
 </dl>
@@ -80,7 +76,7 @@ <h1>Mmhc Estimator<a class="headerlink" href="#mmhc-estimator" title="Permalink
 <dd class="field-even"><p><strong>Estimated model</strong> – The estimated model without the parameterization.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
diff --git a/structure_estimator/pc.html b/structure_estimator/pc.html
index 8291491..617b873 100644
--- a/structure_estimator/pc.html
+++ b/structure_estimator/pc.html
@@ -172,7 +172,7 @@ <h1>PC (Constraint-Based Estimator)<a class="headerlink" href="#pc-constraint-ba
 depending on the value of <cite>return_type</cite> argument.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG, pgmpy.base.PDAG, or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a>(networkx.UndirectedGraph, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a>)</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a>, <a class="reference internal" href="../base/base.html#pgmpy.base.PDAG" title="pgmpy.base.PDAG">pgmpy.base.PDAG</a>, or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a>(networkx.UndirectedGraph, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a>)</p>
 </dd>
 </dl>
 <p class="rubric">References</p>
@@ -223,7 +223,7 @@ <h1>PC (Constraint-Based Estimator)<a class="headerlink" href="#pc-constraint-ba
 pair) results in a I-equivalent Bayesian network DAG.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
diff --git a/structure_estimator/tree.html b/structure_estimator/tree.html
index 7fa325f..b5b8e81 100644
--- a/structure_estimator/tree.html
+++ b/structure_estimator/tree.html
@@ -82,7 +82,7 @@ <h1>Tree Search<a class="headerlink" href="#tree-search" title="Permalink to thi
 <dd class="field-even"><p><strong>Estimated Model</strong> – The estimated model structure.</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>pgmpy.base.DAG</p>
+<dd class="field-odd"><p><a class="reference internal" href="../base/base.html#pgmpy.base.DAG" title="pgmpy.base.DAG">pgmpy.base.DAG</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
diff --git a/tutorial.html b/tutorial.html
index 7b851c4..b8044dc 100644
--- a/tutorial.html
+++ b/tutorial.html
@@ -15,8 +15,7 @@
     <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Introduction to Probabilitic Graphical Models" href="detailed_notebooks/1.%20Introduction%20to%20Probabilistic%20Graphical%20Models.html" />
-    <link rel="prev" title="Extending pgmpy" href="examples/Extending%20pgmpy.html" />
+    <link rel="prev" title="Example Notebooks" href="examples.html" />
    
   <link rel="stylesheet" href="_static/custom.css" type="text/css" />
   
@@ -74,18 +73,7 @@ <h3>Navigation</h3>
 <li class="toctree-l1"><a class="reference internal" href="metrics/metrics.html">Metrics for testing models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="readwrite/base.html">Reading/Writing to File</a></li>
 <li class="toctree-l1"><a class="reference internal" href="examples.html">Example Notebooks</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorial Notebooks</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/1.%20Introduction%20to%20Probabilistic%20Graphical%20Models.html">Introduction to Probabilitic Graphical Models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/2.%20Bayesian%20Networks.html">Bayesian Network</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/3.%20Causal%20Bayesian%20Networks.html">Causal Bayesian Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/4.%20Markov%20Models.html">Markov Networks</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/5.%20Exact%20Inference%20in%20Graphical%20Models.html">Exact Inference in Graphical Models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/6.%20Approximate%20Inference%20in%20Graphical%20Models.html">Approximate Inference in Graphical Models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/7.%20Parameterizing%20with%20Continuous%20Variables.html">Parameterizing with Continuous Variables</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/8.%20Sampling%20Algorithms.html">Sampling In Continuous Graphical Models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/9.%20Reading%20and%20Writing%20from%20pgmpy%20file%20formats.html">Reading and Writing from pgmpy file formats</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/10.%20Learning%20Bayesian%20Networks%20from%20Data.html">Learning Bayesian Networks from Data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="detailed_notebooks/11.%20A%20Bayesian%20Network%20to%20model%20the%20influence%20of%20energy%20consumption%20on%20greenhouse%20gases%20in%20Italy.html">A Bayesian Network to model the influence of energy consumption on greenhouse gases in Italy</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorial Notebooks</a><ul class="simple">
 </ul>
 </li>
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@@ -94,8 +82,7 @@ <h3>Navigation</h3>
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-      <li>Next: <a href="detailed_notebooks/1.%20Introduction%20to%20Probabilistic%20Graphical%20Models.html" title="next chapter">Introduction to Probabilitic Graphical Models</a></li>
+      <li>Previous: <a href="examples.html" title="previous chapter">Example Notebooks</a></li>
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