docs: fix tutorial typos

tutorials/03_density_estimators.ipynb

@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One option is to use one of set of preconfigured density estimators by passing a string in the `density_estimator` keyword argument to the inference object (`NPE` or `NLE`), e.g., \"maf\" to use a Masked Autoregressive Flow, of \"nsf\" to use a Neural Spline Flow with default hyperparameters.\n"
+    "One option is using one of the preconfigured density estimators by passing a string in the `density_estimator` keyword argument to the inference object (`NPE` or `NLE`), e.g., \"maf\" for a Masked Autoregressive Flow, of \"nsf\" for a Neural Spline Flow with default hyperparameters.\n"
    ]
   },
   {
@@ -101,9 +101,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is also possible to pass an `embedding_net` to `posterior_nn()` which learn summary\n",
-    "statistics from high-dimensional simulation outputs. You can find a more detailed\n",
-    "tutorial on this in [04_embedding_networks](04_embedding_networks.md).\n"
+    "It is also possible to pass an `embedding_net` to `posterior_nn()` to automatically\n",
+    "learn summary statistics from high-dimensional simulation outputs. You can find a more\n",
+    "detailed tutorial on this in [04_embedding_networks](04_embedding_networks.md).\n"
    ]
   },
   {

tutorials/08_crafting_summary_statistics.ipynb

@@ -12,7 +12,7 @@
    "metadata": {},
    "source": [
     "Many simulators produce outputs that are high-dimesional. For example, a simulator might\n",
-    "generate a time series or an image. In the tutorial on [04_embedding_networks](04_embedding_networks.md), we discussed how a\n",
+    "generate a time series or an image. In the tutorial [04_embedding_networks](04_embedding_networks.md), we discussed how a\n",
     "neural networks can be used to learn summary statistics from such data. In this\n",
     "notebook, we will instead focus on hand-crafting summary statistics. We demonstrate that\n",
     "the choice of summary statistics can be crucial for the performance of the inference\n",

tutorials/12_iid_data_and_permutation_invariant_embeddings.ipynb

@@ -325,7 +325,9 @@
    "source": [
     "## IID inference with NPE using permutation-invariant embedding nets\n",
     "\n",
-    "For NPE we need to define an embedding net that handles the set-like structure of iid-data, i.e., that it permutation invariant and can handle different number of trials.\n",
+    "For NPE we need to define an embedding net that handles the set-like structure of\n",
+    "iid-data, i.e., a permutation invariant networks that can handle different numbers and\n",
+    "orderings of trials.\n",
     "\n",
     "We implemented several embedding net classes that allow to construct such a permutation- and number-of-trials invariant embedding net.\n",
     "\n",

tutorials/15_importance_sampled_posteriors.ipynb

@@ -58,11 +58,9 @@
     "from torch import ones, eye\n",
     "import torch\n",
     "from torch.distributions import MultivariateNormal\n",
-    "import matplotlib.pyplot as plt\n",
     "\n",
     "from sbi.inference import NPE, ImportanceSamplingPosterior\n",
     "from sbi.utils import BoxUniform\n",
-    "from sbi.inference.potentials.base_potential import BasePotential\n",
     "from sbi.analysis import marginal_plot"
    ]
   },
@@ -71,7 +69,7 @@
    "id": "4808d6d3-cb14-4ecd-a0f5-824bf54a5b01",
    "metadata": {},
    "source": [
-    "We first define a simulator and a prior which both have functions for sampling (as required for SBI) and log_prob evaluations (as required for importance sampling)."
+    "We first define a simulator and a prior which both have a `sample` function (as required for `sbi`) and `log_prob` evaluations (as required for importance sampling)."
    ]
   },
   {
@@ -202,9 +200,16 @@
    "id": "7150cc13-9911-4656-936f-9cafb867eba4",
    "metadata": {},
    "source": [
-    "With the SBI toolbox, importance sampling is a one-liner. SBI supports two methods for importance sampling:\n",
-    "- `\"importance\"`: returns `n_samples` weighted samples (as above) corresponding to `n_samples * sample_efficiency` samples from the posterior. This results in unbiased samples, but the number of effective samples may be small when the SBI estimate is inaccurate.\n",
-    "- `\"sir\"` (sampling-importance-resampling): performs rejection sampling on a batched basis with batch size `oversampling_factor`.  This is a guaranteed way to obtain `N / oversampling_factor` samples, but these may be biased as the weight normalization is not performed across the entire set of samples."
+    "With the `sbi` toolbox, importance sampling is a one-liner. `sbi` supports two methods\n",
+    "for importance sampling:\n",
+    "- `\"importance\"`: returns `n_samples` weighted samples (as above) corresponding to\n",
+    "  `n_samples * sample_efficiency` samples from the posterior. This results in unbiased\n",
+    "  samples, but the number of effective samples may be small when the `sbi` estimate is\n",
+    "  inaccurate.\n",
+    "- `\"sir\"` (sampling-importance-resampling): performs rejection sampling on a batched\n",
+    "  basis with batch size `oversampling_factor`.  This is a guaranteed way to obtain `N /\n",
+    "  oversampling_factor` samples, but these may be biased as the weight normalization is\n",
+    "  not performed across the entire set of samples."
    ]
   },
   {