Fix broken links on website

michaeldeistler · michaeldeistler · commit 70560a3848b3 · 2025-03-18T16:04:56.000+01:00
diff --git a/docs/index.rst b/docs/index.rst
@@ -52,13 +52,13 @@ To get started, install the `sbi` package with:
 
    python -m pip install sbi
 
-for more advanced install options, see our `Install Guide <https://sbi.readthedocs.io/en/latest/installation.html>`.
+for more advanced install options, see our `Install Guide <https://sbi.readthedocs.io/en/latest/installation.html>`_.
 
 Then, check out our material:
 
 -  `Tutorials and Examples <https://sbi.readthedocs.io/en/latest/tutorials.html>`_
 
--  `Reference API <https://sbi.readthedocs.io/en/latest/reference.html>`_
+-  `Reference API <https://sbi.readthedocs.io/en/latest/sbi.html>`_
 
 
 Motivation and approach
@@ -130,7 +130,7 @@ the inference on one particular observation to be more simulation-efficient
 (e.g., SNPE).
 
 Below, we list all implemented methods and their corresponding publications.
-For usage in ``sbi``, see the `Inference API reference <https://sbi.readthedocs.io/en/latest/reference.html>`_
+For usage in ``sbi``, see the `Inference API reference <https://sbi.readthedocs.io/en/latest/sbi.html>`_
 and the `tutorial on implemented methods <https://sbi.readthedocs.io/en/latest/tutorials/16_implemented_methods.html>`_.
 
 
@@ -140,17 +140,14 @@ Posterior estimation (``(S)NPE``)
 - **Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation**
   by Papamakarios & Murray (NeurIPS 2016)
   `PDF <https://papers.nips.cc/paper/6084-fast-free-inference-of-simulation-models-with-bayesian-conditional-density-estimation.pdf>`__
-  `BibTeX <https://papers.nips.cc/paper/6084-fast-free-inference-of-simulation-models-with-bayesian-conditional-density-estimation/bibtex>`__
 
 - **Flexible statistical inference for mechanistic models of neural dynamics**
   by Lueckmann, Goncalves, Bassetto, Öcal, Nonnenmacher & Macke (NeurIPS 2017)
   `PDF <https://papers.nips.cc/paper/6728-flexible-statistical-inference-for-mechanistic-models-of-neural-dynamics.pdf>`__
-  `BibTeX <https://papers.nips.cc/paper/6728-flexible-statistical-inference-for-mechanistic-models-of-neural-dynamics/bibtex>`__
 
 - **Automatic posterior transformation for likelihood-free inference**
   by Greenberg, Nonnenmacher & Macke (ICML 2019)
   `PDF <http://proceedings.mlr.press/v97/greenberg19a/greenberg19a.pdf>`__
-  `BibTeX`__
 
 - **BayesFlow: Learning complex stochastic models with invertible neural networks**
   by Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L., & Köthe, U. (IEEE transactions on neural networks and learning systems 2020)
@@ -174,7 +171,6 @@ Likelihood-estimation (``(S)NLE``)
 - **Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows**
   by Papamakarios, Sterratt & Murray (AISTATS 2019)
   `PDF <http://proceedings.mlr.press/v89/papamakarios19a/papamakarios19a.pdf>`__
-  `BibTeX <https://gpapamak.github.io/bibtex/snl.bib>`__
 
 - **Variational methods for simulation-based inference**
   by Glöckler, Deistler, Macke (ICLR 2022)
diff --git a/docs/tutorials/00_getting_started.ipynb b/docs/tutorials/00_getting_started.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note, you can find the original version of this notebook at [/tutorials/00_getting_started.ipynb](https://github.com/sbi-dev/sbi/blob/main/tutorials/00_getting_started.ipynb) in the `sbi` repository."
+    "Note, you can find the original version of this notebook at [/docs/tutorials/00_getting_started.ipynb](https://github.com/sbi-dev/sbi/blob/main/docs/tutorials/00_getting_started.ipynb) in the `sbi` repository."
    ]
   },
   {
@@ -60,7 +60,7 @@
     "2. a candidate (mechanistic) model - _the simulator_  \n",
     "3. prior knowledge or constraints on model parameters - _the prior_\n",
     "\n",
-    "If you are new to simulation-based inference, please first read the information on the [homepage of the website](https://sbi-dev.github.io/sbi/) to familiarise with the motivation and relevant terms."
+    "If you are new to simulation-based inference, please first read the information on the [homepage of the website](https://sbi.readthedocs.io/en/latest/index.html) to familiarise with the motivation and relevant terms."
    ]
   },
   {
@@ -138,9 +138,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Note: In `sbi` version 0.23.0, we renamed all inference classes from, e.g., `SNPE`, to `NPE` (i.e., we removed the `S` prefix). The functionality of the classes remains the same. The `NPE` class handles both the amortized (as shown in this tutorial) and sequential (as shown [here](https://sbi-dev.github.io/sbi/tutorial/02_multiround_inference/)) versions of neural posterior estimation. An alias for `SNPE` still exists for backwards compatibility.\n",
+    "> Note: In `sbi` version 0.23.0, we renamed all inference classes from, e.g., `SNPE`, to `NPE` (i.e., we removed the `S` prefix). The functionality of the classes remains the same. The `NPE` class handles both the amortized (as shown in this tutorial) and sequential (as shown [here](https://sbi.readthedocs.io/en/latest/tutorials/02_multiround_inference.html)) versions of neural posterior estimation. An alias for `SNPE` still exists for backwards compatibility.\n",
     "\n",
-    "> Note: This is where you could specify an alternative inference object such as NRE for ratio estimation or NLE for likelihood estimation. Here, you can see [all implemented methods](https://sbi-dev.github.io/sbi/latest/tutorials/16_implemented_methods/)."
+    "> Note: This is where you could specify an alternative inference object such as NRE for ratio estimation or NLE for likelihood estimation. Here, you can see [all implemented methods](https://sbi.readthedocs.io/en/latest/tutorials/16_implemented_methods.html)."
    ]
   },
   {
@@ -456,11 +456,11 @@
     "## Next steps\n",
     "\n",
     "To learn more about the capabilities of `sbi`, you can head over to the tutorial\n",
-    "[01_gaussian_amortized](01_gaussian_amortized.md), for inferring parameters for multiple\n",
+    "[01_gaussian_amortized](https://sbi.readthedocs.io/en/latest/tutorials/01_gaussian_amortized.html), for inferring parameters for multiple\n",
     "observations without retraining.\n",
     "\n",
     "Alternatively, for an example with an __actual__ simulator, you can read our example\n",
-    "for a scientific simulator from neuroscience under [Example_00_HodgkinHuxleyModel](Example_00_HodgkinHuxleyModel.md)."
+    "for a scientific simulator from neuroscience under [Example_00_HodgkinHuxleyModel](https://sbi.readthedocs.io/en/latest/tutorials/Example_00_HodgkinHuxleyModel.html)."
    ]
   }
  ],
@@ -480,7 +480,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/01_gaussian_amortized.ipynb b/docs/tutorials/01_gaussian_amortized.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note, you can find the original version of this notebook at [tutorials/01_gaussian_amortized.ipynb](https://github.com/sbi-dev/sbi/blob/main/tutorials/01_gaussian_amortized.ipynb) in the `sbi` repository."
+    "Note, you can find the original version of this notebook at [docs/tutorials/01_gaussian_amortized.ipynb](https://github.com/sbi-dev/sbi/blob/main/docs/tutorials/01_gaussian_amortized.ipynb) in the `sbi` repository."
    ]
   },
   {
@@ -20,7 +20,7 @@
    "source": [
     "In this tutorial, we introduce **amortization** that is the capability to evaluate the posterior for different observations without having to re-run inference.\n",
     "\n",
-    "We will demonstrate how `sbi` can infer an amortized posterior for the illustrative linear Gaussian example introduced in [Getting Started](https://sbi-dev.github.io/sbi/latest/tutorials/00_getting_started/), that takes in 3 parameters ($\\theta$). "
+    "We will demonstrate how `sbi` can infer an amortized posterior for the illustrative linear Gaussian example introduced in [Getting Started](https://sbi.readthedocs.io/en/latest/tutorials/00_getting_started.html), that takes in 3 parameters ($\\theta$). "
    ]
   },
   {
@@ -214,10 +214,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Next steps\n",
+    "## Next steps\n",
     "\n",
     "Now that you got familiar with amortization and are probably good to go and have a first shot at applying `sbi` to your own inference problem. If you want to learn more, we recommend checking out our tutorial\n",
-    "[02_multiround_inference](02_multiround_inference.md) which aims to make inference for a single observation more sampling efficient."
+    "[02_multiround_inference](https://sbi.readthedocs.io/en/latest/tutorials/02_multiround_inference.html) which aims to make inference for a single observation more sampling efficient."
    ]
   }
  ],
@@ -237,7 +237,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/02_multiround_inference.ipynb b/docs/tutorials/02_multiround_inference.ipynb
@@ -17,7 +17,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note, you can find the original version of this notebook at [tutorials/02_multiround_inference.ipynb](https://github.com/sbi-dev/sbi/blob/main/tutorials/02_multiround_inference.ipynb) in the `sbi` repository.\n"
+    "Note, you can find the original version of this notebook at [docs/tutorials/02_multiround_inference.ipynb](https://github.com/sbi-dev/sbi/blob/main/docs/tutorials/02_multiround_inference.ipynb) in the `sbi` repository.\n"
    ]
   },
   {
@@ -54,14 +54,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Neural network successfully converged after 196 epochs."
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using SNPE-C with atomic loss\n",
+      " Neural network successfully converged after 196 epochs.Using SNPE-C with atomic loss\n",
       " Neural network successfully converged after 37 epochs."
      ]
     }
@@ -173,14 +166,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Neural network successfully converged after 277 epochs."
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using SNPE-C with atomic loss\n",
+      " Neural network successfully converged after 277 epochs.Using SNPE-C with atomic loss\n",
       " Neural network successfully converged after 35 epochs."
      ]
     }
@@ -260,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/03_density_estimators.ipynb b/docs/tutorials/03_density_estimators.ipynb
@@ -18,7 +18,7 @@
     "[`nflows`](https://github.com/bayesiains/nflows/) (via `pyknos`) or [`zuko`](https://github.com/probabilists/zuko). \n",
     "\n",
     "For all options, check the API reference\n",
-    "[here](https://sbi-dev.github.io/sbi/reference/models/).\n"
+    "[here](https://sbi.readthedocs.io/en/latest/reference/sbi.models.html).\n"
    ]
   },
   {
@@ -113,7 +113,7 @@
    "source": [
     "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"
+    "detailed tutorial on this in [04_embedding_networks](https://sbi.readthedocs.io/en/latest/tutorials/04_embedding_networks.html).\n"
    ]
   },
   {
@@ -137,15 +137,8 @@
     "- `loss(input, condition, **kwargs)`: Return the loss for training the density estimator.\n",
     "- `sample(sample_shape, condition, **kwargs)`: Return samples from the density estimator.\n",
     "\n",
-    "See more information on the [Reference API page](https://sbi-dev.github.io/sbi/reference/models/#sbi.neural_nets.density_estimators.ConditionalDensityEstimator)."
+    "See more information on the [Reference API page](https://sbi.readthedocs.io/en/latest/sbi.html)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -164,7 +157,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/04_embedding_networks.ipynb b/docs/tutorials/04_embedding_networks.ipynb
@@ -6,8 +6,7 @@
    "source": [
     "# Embedding nets for observations\n",
     "\n",
-    "!!! note\n",
-    "    You can find the original version of this notebook at [tutorials/04_embedding_networks.ipynb](https://github.com/sbi-dev/sbi/blob/main/tutorials/04_embedding_networks.ipynb) in the `sbi` repository.\n",
+    "Note, you can find the original version of this notebook at [docs/tutorials/04_embedding_networks.ipynb](https://github.com/sbi-dev/sbi/blob/main/docs/tutorials/04_embedding_networks.ipynb) in the `sbi` repository.\n",
     "\n",
     "## Introduction\n",
     "\n",
@@ -251,8 +250,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "!!! note\n",
-    "    See [here](https://github.com/sbi-dev/sbi/blob/main/sbi/neural_nets/embedding_nets.py) for details on all hyperparametes for each available embedding net in `sbi`\n",
+    "See [here](https://github.com/sbi-dev/sbi/blob/main/sbi/neural_nets/embedding_nets.py) for details on all hyperparametes for each available embedding net in `sbi`\n",
     "\n",
     "## The inference procedure\n",
     "\n",
@@ -432,7 +430,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/05_conditional_distributions.ipynb b/docs/tutorials/05_conditional_distributions.ipynb
@@ -15,7 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note, you can find the original version of this notebook at [tutorials/05_conditional_distributions.ipynb](https://github.com/sbi-dev/sbi/blob/main/tutorials/05_conditional_distributions.ipynb) in the `sbi` repository.\n"
+    "Note, you can find the original version of this notebook at [docs/tutorials/05_conditional_distributions.ipynb](https://github.com/sbi-dev/sbi/blob/main/docs/tutorials/05_conditional_distributions.ipynb) in the `sbi` repository.\n"
    ]
   },
   {
diff --git a/docs/tutorials/06_restriction_estimator.ipynb b/docs/tutorials/06_restriction_estimator.ipynb
@@ -231,13 +231,7 @@
      "text": [
       "The `RestrictedPrior` rejected 53.2%\n",
       "                of prior samples. You will get a speed-up of\n",
-      "                113.8%.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                113.8%.\n",
       "Simulation outputs:  tensor([[ 1.7930,  1.5322],\n",
       "        [ 1.6024,  1.6551],\n",
       "        [ 0.0756,  2.4023],\n",
@@ -329,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/tutorials/08_crafting_summary_statistics.ipynb b/docs/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 [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](https://sbi.readthedocs.io/en/latest/tutorials/04_embedding_networks.html), 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",
@@ -599,7 +599,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/tutorials/11_diagnostics_simulation_based_calibration.ipynb b/docs/tutorials/11_diagnostics_simulation_based_calibration.ipynb
@@ -10,7 +10,7 @@
     "the estimator should be made subject to several **diagnostic tests**. This needs to be\n",
     "performed before being used for inference given the actual observed data. _Posterior\n",
     "Predictive Checks_ (see [10_diagnostics_posterior_predictive_checks\n",
-    "tutorial](10_diagnostics_posterior_predictive_checks.md)) provide one way to \"critique\" a trained\n",
+    "tutorial](http://localhost:8000/tutorials/10_diagnostics_posterior_predictive_checks.html)) provide one way to \"critique\" a trained\n",
     "estimator based on its predictive performance. Another important approach to such\n",
     "diagnostics is simulation-based calibration as developed by [Cook et al,\n",
     "2006](https://www.tandfonline.com/doi/abs/10.1198/106186006X136976) and [Talts et al,\n",
@@ -909,7 +909,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "sbi-dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/tutorials/12_iid_data_and_permutation_invariant_embeddings.ipynb b/docs/tutorials/12_iid_data_and_permutation_invariant_embeddings.ipynb
@@ -599,7 +599,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/tutorials/13_diagnostics_lc2st.ipynb b/docs/tutorials/13_diagnostics_lc2st.ipynb
@@ -4,14 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Local Classifier Two-Sample Tests ($\\ell$-C2ST)\n",
+    "# Local Classifier Two-Sample Tests (L-C2ST)\n",
     "\n",
     "\n",
     " After a density estimator has been trained with simulated data to obtain a posterior, the estimator should be made subject to several diagnostic tests. This diagnostic should be performed before the posterior is used for inference given the actual observed data. \n",
     "    \n",
-    "*Posterior Predictive Checks* (see [tutorial 10](10_diagnostics_posterior_predictive_checks.md)) provide one way to \"critique\" a trained estimator via its predictive performance. \n",
+    "*Posterior Predictive Checks* (see [tutorial 10](https://sbi.readthedocs.io/en/latest/tutorials/10_diagnostics_posterior_predictive_checks.html)) provide one way to \"critique\" a trained estimator via its predictive performance. \n",
     "    \n",
-    "Another approach is *Simulation-Based Calibration* (SBC, see [tutorial 11](11_diagnostics_simulation_based_calibration.md)). SBC evaluates whether the estimated posterior is balanced, i.e., neither over-confident nor under-confident. These checks are performed ***in expectation (on average) over the observation space***, i.e. they are performed on a set of $(\\theta,x)$ pairs sampled from the joint distribution over simulator parameters $\\theta$ and corresponding observations $x$. As such, SBC is a ***global validation method*** that can be viewed as a necessary condition (but not sufficient) for a valid inference algorithm: If SBC checks fail, this tells you that your inference is invalid. If SBC checks pass, *this is no guarantee that the posterior estimation is working*.\n",
+    "Another approach is *Simulation-Based Calibration* (SBC, see [tutorial 11](https://sbi.readthedocs.io/en/latest/tutorials/11_diagnostics_simulation_based_calibration.html)). SBC evaluates whether the estimated posterior is balanced, i.e., neither over-confident nor under-confident. These checks are performed ***in expectation (on average) over the observation space***, i.e. they are performed on a set of $(\\theta,x)$ pairs sampled from the joint distribution over simulator parameters $\\theta$ and corresponding observations $x$. As such, SBC is a ***global validation method*** that can be viewed as a necessary condition (but not sufficient) for a valid inference algorithm: If SBC checks fail, this tells you that your inference is invalid. If SBC checks pass, *this is no guarantee that the posterior estimation is working*.\n",
     "\n",
     "**Local Classifier Two-Sample Tests** ($\\ell$-C2ST) as developed by [Linhart et al, 2023](https://arxiv.org/abs/2306.03580) present a new ***local validation method*** that allows to evaluate the correctness of the posterior estimator ***at a fixed observation***, i.e. they work on a single $(\\theta,x)$ pair. They provide necessary *and sufficient* conditions for the validity of the SBI algorithm, as well as easy-to-interpret qualitative and quantitative diagnostics. \n",
     "    \n",
@@ -881,7 +881,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.4"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/tutorials/14_mcmc_diagnostics_with_arviz.ipynb b/docs/tutorials/14_mcmc_diagnostics_with_arviz.ipynb
diff --git a/docs/tutorials/15_importance_sampled_posteriors.ipynb b/docs/tutorials/15_importance_sampled_posteriors.ipynb
diff --git a/docs/tutorials/18_training_interface.ipynb b/docs/tutorials/18_training_interface.ipynb
diff --git a/docs/tutorials/Example_00_HodgkinHuxleyModel.ipynb b/docs/tutorials/Example_00_HodgkinHuxleyModel.ipynb
diff --git a/docs/tutorials/Example_01_DecisionMakingModel.ipynb b/docs/tutorials/Example_01_DecisionMakingModel.ipynb

Original file line number	Diff line number	Diff line change
`@@ -11,7 +11,7 @@`
`11`	`11`	`"cell_type": "markdown",`
`12`	`12`	`"metadata": {},`
`13`	`13`	`"source": [`
`14`		- "Note, you can find the original version of this notebook at [/tutorials/00_getting_started.ipynb](https://github.com/sbi-dev/sbi/blob/main/tutorials/00_getting_started.ipynb) in the `sbi` repository."
	`14`	+ "Note, you can find the original version of this notebook at [/docs/tutorials/00_getting_started.ipynb](https://github.com/sbi-dev/sbi/blob/main/docs/tutorials/00_getting_started.ipynb) in the `sbi` repository."
`15`	`15`	`]`
`16`	`16`	`},`
`17`	`17`	`{`
`@@ -60,7 +60,7 @@`
`60`	`60`	`"2. a candidate (mechanistic) model - _the simulator_ \n",`
`61`	`61`	`"3. prior knowledge or constraints on model parameters - _the prior_\n",`
`62`	`62`	`"\n",`
`63`		`- "If you are new to simulation-based inference, please first read the information on the [homepage of the website](https://sbi-dev.github.io/sbi/) to familiarise with the motivation and relevant terms."`
	`63`	`+ "If you are new to simulation-based inference, please first read the information on the [homepage of the website](https://sbi.readthedocs.io/en/latest/index.html) to familiarise with the motivation and relevant terms."`
`64`	`64`	`]`
`65`	`65`	`},`
`66`	`66`	`{`
`@@ -138,9 +138,9 @@`
`138`	`138`	`"cell_type": "markdown",`
`139`	`139`	`"metadata": {},`
`140`	`140`	`"source": [`
`141`		- "> Note: In `sbi` version 0.23.0, we renamed all inference classes from, e.g., `SNPE`, to `NPE` (i.e., we removed the `S` prefix). The functionality of the classes remains the same. The `NPE` class handles both the amortized (as shown in this tutorial) and sequential (as shown [here](https://sbi-dev.github.io/sbi/tutorial/02_multiround_inference/)) versions of neural posterior estimation. An alias for `SNPE` still exists for backwards compatibility.\n",
	`141`	+ "> Note: In `sbi` version 0.23.0, we renamed all inference classes from, e.g., `SNPE`, to `NPE` (i.e., we removed the `S` prefix). The functionality of the classes remains the same. The `NPE` class handles both the amortized (as shown in this tutorial) and sequential (as shown [here](https://sbi.readthedocs.io/en/latest/tutorials/02_multiround_inference.html)) versions of neural posterior estimation. An alias for `SNPE` still exists for backwards compatibility.\n",
`142`	`142`	`"\n",`
`143`		`- "> Note: This is where you could specify an alternative inference object such as NRE for ratio estimation or NLE for likelihood estimation. Here, you can see [all implemented methods](https://sbi-dev.github.io/sbi/latest/tutorials/16_implemented_methods/)."`
	`143`	`+ "> Note: This is where you could specify an alternative inference object such as NRE for ratio estimation or NLE for likelihood estimation. Here, you can see [all implemented methods](https://sbi.readthedocs.io/en/latest/tutorials/16_implemented_methods.html)."`
`144`	`144`	`]`
`145`	`145`	`},`
`146`	`146`	`{`
`@@ -456,11 +456,11 @@`
`456`	`456`	`"## Next steps\n",`
`457`	`457`	`"\n",`
`458`	`458`	"To learn more about the capabilities of `sbi`, you can head over to the tutorial\n",
`459`		`- "[01_gaussian_amortized](01_gaussian_amortized.md), for inferring parameters for multiple\n",`
	`459`	`+ "[01_gaussian_amortized](https://sbi.readthedocs.io/en/latest/tutorials/01_gaussian_amortized.html), for inferring parameters for multiple\n",`
`460`	`460`	`"observations without retraining.\n",`
`461`	`461`	`"\n",`
`462`	`462`	`"Alternatively, for an example with an __actual__ simulator, you can read our example\n",`
`463`		`- "for a scientific simulator from neuroscience under [Example_00_HodgkinHuxleyModel](Example_00_HodgkinHuxleyModel.md)."`
	`463`	`+ "for a scientific simulator from neuroscience under [Example_00_HodgkinHuxleyModel](https://sbi.readthedocs.io/en/latest/tutorials/Example_00_HodgkinHuxleyModel.html)."`
`464`	`464`	`]`
`465`	`465`	`}`
`466`	`466`	`],`
`@@ -480,7 +480,7 @@`
`480`	`480`	`"name": "python",`
`481`	`481`	`"nbconvert_exporter": "python",`
`482`	`482`	`"pygments_lexer": "ipython3",`
`483`		`- "version": "3.10.14"`
	`483`	`+ "version": "3.12.4"`
`484`	`484`	`}`
`485`	`485`	`},`
`486`	`486`	`"nbformat": 4,`
Original file line number	Diff line number	Diff line change
`@@ -15,7 +15,7 @@`
`15`	`15`	`"cell_type": "markdown",`
`16`	`16`	`"metadata": {},`
`17`	`17`	`"source": [`
`18`		- "Note, you can find the original version of this notebook at [tutorials/05_conditional_distributions.ipynb](https://github.com/sbi-dev/sbi/blob/main/tutorials/05_conditional_distributions.ipynb) in the `sbi` repository.\n"
	`18`	+ "Note, you can find the original version of this notebook at [docs/tutorials/05_conditional_distributions.ipynb](https://github.com/sbi-dev/sbi/blob/main/docs/tutorials/05_conditional_distributions.ipynb) in the `sbi` repository.\n"
`19`	`19`	`]`
`20`	`20`	`},`
`21`	`21`	`{`