diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
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+++ b/CONTRIBUTING.md
@@ -0,0 +1,34 @@
+# Contribution guidelines for BayesValidRox
+Thank you for investing your time in contributing to our project!
+In this guide you will get an overview of the contribution workflow from opening an issue, creating a PR, reviewing, and merging the PR.
+
+To get an overview of the project you can read the [README](https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/master/README.md?ref_type=heads) file and have a look at the BayesValidRox website.
+
+## Issues
+If you know what you’re going to work on, see if an issue exists. 
+If a related issue doesn't exist, you can open a new issue using a relevant issue form.
+
+Scan through existing issues to find one that interests you. You can narrow down the search using labels as filters. 
+When you find an issue, leave a comment on the issue you want to work on. 
+This helps to let others know that you will be working on that issue.
+
+## Testing and Examples
+Any changes that you make while working on your chosen issue should be reflected in the [tests](https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/tree/master/tests?ref_type=heads).
+This includes updating the tests that are affected by changes in the source code, as well as adding tests for new features.
+
+The same holds true for the [examples](https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/tree/master/examples?ref_type=heads).
+If you are adding new functionalities and features, a new example should be added that shows how these features are used.
+
+
+## Submitting changes
+Please send a GitHub Pull Request with a clear list of what you've done. 
+When you send a pull request, it would be great if you include tests and examples that reflect your changes. 
+We can always use more test coverage. 
+Please follow our coding conventions (below) and make sure all of your commits are atomic (one feature per commit).
+
+Always write a clear log message for your commits. One-line messages are fine for small changes, but bigger changes should look like this:
+
+$ git commit -m "A brief summary of the commit
+> 
+> A paragraph describing what changed and its impact."
+
diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle
index b2e0863886180fc1aa05b2007d0471193138f92b..0df551bdab8506d738101801358d557f1ca8e338 100644
Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html
index 1598c3ae4c69b0cb1637d65c06d031727fb3124f..a8fbf99526166e7a511815115a03b9f56209f850 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html
@@ -166,8 +166,11 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../packagedescription.html">USER GUIDE</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of USER GUIDE</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../input_description.html">Priors, input space and experimental design</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html
index 1fa6677318b8af94fc93ac68353acfb3ccdebfed..d5f4bdc76f24d30fb6c49cd17b346870ba53c969 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html
@@ -166,8 +166,11 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../packagedescription.html">USER GUIDE</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of USER GUIDE</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../input_description.html">Priors, input space and experimental design</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html
index 37db0dba49b9ccf95ea6dc938ad174f9ffbded54..2b9f5ffba8dc6c97279d7eaa505046c32f5ad279 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html
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@@ -166,8 +166,11 @@
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 <li class="toctree-l1 has-children"><a class="reference internal" href="../packagedescription.html">USER GUIDE</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of USER GUIDE</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../input_description.html">Priors, input space and experimental design</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html
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 <li class="toctree-l2"><a class="reference internal" href="../input_description.html">Priors, input space and experimental design</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.discrepancy.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.discrepancy.html
index dbded702238ab5044ef67d43f65697212115692d..3335850940b773ac5fb5ad777466f1be6e2d63fc 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.discrepancy.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.discrepancy.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.html
index 7f936229549e4d0d37d5946401a70921e408f350..c7fdddf41ddfa00abc595774d5e201de4af10b93 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html
index bb9aba4867fd641cd202d1a09896ee91e935398d..1c766909b66ee0fbeee63c3605370175ed825f8a 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html
@@ -166,8 +166,11 @@
   <ul class="current">
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.html
index 2dd7b60ddc2d68041d4fc6369011f3658074653d..d00bc5717899bfaeeff4c2839d4f6277d56ea7ea 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.html b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.html
index 3906931dc8849acc3fb755883e5031d39928212d..29b44ce99ee43c3613f96fa97f2bbdff3c0e6f26 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.bayes_inference.mcmc.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.html b/docs/build/html/_autosummary/bayesvalidrox.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.post_processing.html b/docs/build/html/_autosummary/bayesvalidrox.post_processing.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html b/docs/build/html/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html
index 23bc022a5c750b43f38ccbb9768dffc1108740e5..1e9cfdbeccd952605a0e0af15ea2bbc7674dc461 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.post_processing.post_processing.html b/docs/build/html/_autosummary/bayesvalidrox.post_processing.post_processing.html
index 752be7e7b4556b87c2f5f1f455d399a40c939bc4..1c0ee07983681c2921eb574305c332b34e5b33e1 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.post_processing.post_processing.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.pylink.html b/docs/build/html/_autosummary/bayesvalidrox.pylink.html
index a92f4eadd04edff924bd7549ff14d2ee09b696d2..d3b4ddb9d22a55fa65b74b8480ed483ad79e5ebc 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.pylink.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.pylink.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html b/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html
index 3fd01d6459741ff9780d56c32c7d6589b42fdb9a..868fd803201d3bf3f5b07955c8755ebf4ecd837f 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.html b/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.html
index e3ae83058146d5bc708957012e6cc9f14f78ce35..fd29ae01185ad77c3ba5c349b2f79b87053dbe87 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.within_range.html b/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.within_range.html
index dede4fc9a6c87dde775b8e12c89e0af8c2037893..d13e5d7afc4d30a74815716be03c5a653f7a8b2d 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.within_range.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.pylink.pylink.within_range.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.html
index f2c59c45bddc8695b07609fed2ada06f79053fff..88166ae557a8ac4d2c5b58049ce6421b53f6da77 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html
index 208623027e688f37bd944c064fbbdb309393864f..dc183669e991009b0b317be369c56cfe4877392b 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.html
index 44563edabcfe5d9bf06900bf69fa7bd38d85c866..7fd894a2b05c2238263ca95d94ebe67ef701af04 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html
index 0841b4ece045228c72b96369727f8bcc4651659a..bb8064c4ace44d3d188f6170d24d3752ab531710 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html
index cad9e1c779c38955764be5f15a8254733e93aaec..f0262935e671136434b3ace548117b639faaf866 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html
index 78b455a0737ee544faf7d8b93a17d2c06fe9c583..faf9eaf24d776041650e1e6dcb3387562ea92652 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../input_description.html">Priors, input space and experimental design</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.html
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 <li class="toctree-l2"><a class="reference internal" href="../input_description.html">Priors, input space and experimental design</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html
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 <li class="toctree-l2"><a class="reference internal" href="../input_description.html">Priors, input space and experimental design</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.html
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.html
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html
index 152ac0cc69ec773c28e708006de76d8a3d5e5731..58709347de4066cf52315788d2b403067aed34e2 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.html
index f21e500d43949d3d25f7d89322d7fe38ce8fa249..c9556e4315c0343672ac2692d4c7b940a44de4d1 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.html
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@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.html
index 9de0b16b2bd5ef9799288325403b0a947dec0a4d..0a54834e033be55f36faa4bf9341ac15ce40fdf6 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html
index cd723f3ffb0336cfde96ee808f77b8215c75518e..4496b2d8f717108f8ae3821169740ccaeae2cbd7 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exploration.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exploration.html
index e1ac08e7f9c7bcbfe7cb8e00c036b85b02bbc647..5482569e1e7bd5a8e74fc2b8952952af366bd1d5 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exploration.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exploration.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.html
index c03a28785096bff0540ef5b6484f856f1ec11868..5a03208c86819cc3e68adf1b35a9d5ac9c0ce90b 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.html
index c539a49fc5f6ff8d81d886a2b324ad58e05b4359..a950df2ff0a2fc8f4c0aa58deb6e0e70982e5e70 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.html
index 23c6ccaf9b1c62ed712bb0a2518fa1a1bf154046..96400f3335ee9fefce7b8c74b73b5d7b6540132d 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.glexindex.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.html
index b7b7a8357254d992c619855acd6eb68251b44dac..4312aa2528b5efb983f248efd68be7c01872f541 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.html
@@ -169,7 +169,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html
index 3267f5a5bcfeaf030c86c582e71ff2ceb886289f..b5eb76fa2d343182c07caac085a2d114701e1ff4 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.input_space.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.input_space.html
index a6709b5e2bfd178a344872bdc2040762bda3e381..cc003dd63acb7bd7dff4be0e618139ff182668c7 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.input_space.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.input_space.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html
index b9176038b945603d3917133a8d6619b535d452a1..95f8b8e450bb17bdf54da7c89e48b1e02dea8427 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html
index 76b367e00171da1f16dc39466870163311111c5c..fca0dcb0d866e58c0d2b0f1754daa5b673b05255 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.html
index 029c319cd8cb5349291475ca63bfe1c046b41b1d..d5c3b12c17f42351a7d5ce1b97793db0d014d893 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.inputs.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html
index a779a0005a683cae0baf365641d5f7d8f335a6bc..3e5c7122e259e03f7696886326d870b01236c5dc 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html
index 1786db353d68b904ebd8feb4ed5c1aef0baa68af..274d8319d15ce6f02bb11570b0f04de5d05ec170 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html
index 80f8889317e166a036ab6bb25931db0683fe574f..f159c07b7d16bbea3883c98dccd211d0ce7e19a3 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html
index 416904a84e50b7320c99b2411370941863936a6f..0ff5711b1197cef309e92dba0306360e8dd2302e 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html
index fd8ef6ea88ac3077dfb223e89be8b77e89aeeddd..f1a15392db7b92fd33289c6a80f192bf29ad688c 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html
index a56113eb822068c738c5395254282274a10f99e2..ff1c030d120352eb3d7e6361764305295eb5ea81 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html
index ec46718c4c960aa78bc09bfb81be20bccb12e5de..dc6b45be7b458da14c03fc147d90f0fc698b1eab 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html
index 57bdb940db768c2ea82143869752f165c38690f6..39bed6efb714b788a749f3800f756d641d8ab6d8 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html
index 9f4ce9efa7114ac11c39ebdcad45521f09bc7e37..49744e8097ad29d88b38c04284f137b188ee2946 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html
index ea62a231fa4d5ce2bb0190d26cf223d2e0e149ef..a047d2ad8298fa5a806499059658a45f4082a040 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html
index 290dd7f7294dd2d94aebf4979abd8e60da4224e7..1b72a962cb784fc83d165f7b78c1ff34f2012271 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html
@@ -166,8 +166,11 @@
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+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../bayes_description.html">Bayesian inference and multi-model comparison</a></li>
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html
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--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html
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 <li class="toctree-l2"><a class="reference internal" href="../input_description.html">Priors, input space and experimental design</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_description.html">Models</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="input_description.html">Priors, input space and experimental design</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="input_description.html">Priors, input space and experimental design</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
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\ No newline at end of file
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31, 34, 35, 36, 39, 44, 47, 52, 53, 55, 58, 60, 63, 72, 74, 80, 81], "x1": 16, "x_1": [49, 50, 73, 81], "x_2": [73, 81], "x_3": 73, "x_axi": 13, "x_can": 29, "x_i": [44, 80], "x_mc": 29, "x_new": 29, "x_valu": [16, 20, 74], "xi": 16, "xiu": 81, "xlabel": 13, "xnew": 29, "xx": 16, "xxa": 55, "xya": 55, "y": [7, 9, 24, 25, 26, 39, 52, 53, 55, 60, 61, 63], "y_hat": [25, 26, 29, 52, 55, 58], "y_i": 80, "y_pc_val": 20, "y_pc_val_std": 20, "y_pred": [24, 25, 26, 52, 55, 60], "y_true": [24, 25, 26, 52, 55], "y_val": 20, "ye": [16, 65, 68, 78], "yet": 10, "yew": 29, "you": [7, 13, 16, 24, 25, 26, 52, 55, 66, 69, 73, 75, 76, 78], "young": 68, "your": [13, 69, 73, 76, 77], "yr": [69, 73], "z": 81, "zero": [30, 52, 55, 58], "zeromean": 7, "zip": 16, "zip_subdir": 16, "zurich": 33}, "titles": ["bayesvalidrox", "bayesvalidrox.bayes_inference", "bayesvalidrox.bayes_inference.bayes_inference", "bayesvalidrox.bayes_inference.bayes_inference.BayesInference", "bayesvalidrox.bayes_inference.bayes_model_comparison", "bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison", "bayesvalidrox.bayes_inference.discrepancy", "bayesvalidrox.bayes_inference.discrepancy.Discrepancy", "bayesvalidrox.bayes_inference.mcmc", "bayesvalidrox.bayes_inference.mcmc.MCMC", "bayesvalidrox.bayes_inference.mcmc.gelman_rubin", "bayesvalidrox.post_processing", "bayesvalidrox.post_processing.post_processing", "bayesvalidrox.post_processing.post_processing.PostProcessing", "bayesvalidrox.pylink", "bayesvalidrox.pylink.pylink", "bayesvalidrox.pylink.pylink.PyLinkForwardModel", "bayesvalidrox.pylink.pylink.within_range", "bayesvalidrox.surrogate_models", "bayesvalidrox.surrogate_models.adaptPlot", "bayesvalidrox.surrogate_models.adaptPlot.adaptPlot", "bayesvalidrox.surrogate_models.apoly_construction", "bayesvalidrox.surrogate_models.apoly_construction.apoly_construction", "bayesvalidrox.surrogate_models.bayes_linear", "bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.gamma_mean", "bayesvalidrox.surrogate_models.engine", "bayesvalidrox.surrogate_models.engine.Engine", "bayesvalidrox.surrogate_models.engine.hellinger_distance", "bayesvalidrox.surrogate_models.engine.logpdf", "bayesvalidrox.surrogate_models.engine.subdomain", "bayesvalidrox.surrogate_models.eval_rec_rule", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis", "bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs", "bayesvalidrox.surrogate_models.exp_designs", "bayesvalidrox.surrogate_models.exp_designs.ExpDesigns", "bayesvalidrox.surrogate_models.exp_designs.check_ranges", "bayesvalidrox.surrogate_models.exploration", "bayesvalidrox.surrogate_models.exploration.Exploration", "bayesvalidrox.surrogate_models.glexindex", "bayesvalidrox.surrogate_models.glexindex.cross_truncate", "bayesvalidrox.surrogate_models.glexindex.glexindex", "bayesvalidrox.surrogate_models.input_space", "bayesvalidrox.surrogate_models.input_space.InputSpace", "bayesvalidrox.surrogate_models.inputs", "bayesvalidrox.surrogate_models.inputs.Input", "bayesvalidrox.surrogate_models.inputs.Marginal", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr", "bayesvalidrox.surrogate_models.reg_fast_ard", "bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD", "bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions", "bayesvalidrox.surrogate_models.reg_fast_laplace", "bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace", "bayesvalidrox.surrogate_models.surrogate_models", "bayesvalidrox.surrogate_models.surrogate_models.MetaModel", "bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error", "bayesvalidrox.surrogate_models.surrogate_models.create_psi", "bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator", "Active learning: iteratively expanding the training set", "Example: Analytical function", "API", "Bayesian inference and multi-model comparison", "Example: beam", "Example: borehole", "EXAMPLES", "Surrogate-assisted\u00a0Bayesian validation of computational models", "Priors, input space and experimental design", "Example: ishigami", "Models", "Example: model comparison", "Example: OHagan-function", "USER GUIDE", "Example: pollution", "Postprocessing", "Training surrogate models", "TUTORIAL"], "titleterms": {"1": 75, "3": 75, "activ": 64, "adaptplot": [19, 20], "also": 71, "an": 81, "analyt": 65, "api": 66, "apoly_construct": [21, 22], "argument": [29, 42], "assist": 71, "attribut": [3, 5, 7, 9, 13, 16, 25, 26, 39, 42, 47, 49, 50, 52, 55, 58, 60], "bay": [], "bayes_infer": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "bayes_linear": [23, 24, 25, 26, 27], "bayes_model_comparison": [4, 5], "bayesian": [67, 71, 81], "bayesianlinearregress": 24, "bayesinfer": 3, "bayesmodelcomparison": 5, "bayesvalidrox": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], "beam": 68, "borehol": 69, "check_rang": 40, "choic": [65, 68, 69, 73, 75, 76, 78], "class": 80, "comparison": [67, 75], "comput": 71, "contact": 71, "content": 71, "corr": 53, "corr_loocv_error": 61, "create_psi": 62, "cross_trunc": 44, "data": [65, 68, 69, 73, 76, 78, 81], "defin": 81, "descript": [], "design": [72, 81], "discrep": [6, 7, 65, 68], "eblinearregress": 25, "engin": [28, 29, 30, 31, 32, 80, 81], "eval_rec_rul": [33, 34, 35, 36, 37], "eval_rec_rule_arbitrari": 35, "eval_univ_basi": 36, "exampl": [49, 65, 68, 69, 70, 72, 73, 74, 75, 76, 78, 79, 80], "exp_design": [38, 39, 40], "expand": 64, "expdesign": 39, "experiment": [72, 81], "explor": [41, 42], "function": [65, 76], "further": 71, "gamma_mean": 27, "gaussian_process_emul": 63, "gelman_rubin": 10, "glexindex": [43, 44, 45], "guid": 77, "hellinger_dist": 30, "import": 81, "indic": 71, "infer": [67, 81], "input": [48, 49, 50, 72, 81], "input_spac": [46, 47], "inputspac": 47, "instal": [71, 77], "introductori": [], "ishigami": 73, "iter": 64, "l2_model": 75, "learn": 64, "librari": 81, "licens": 71, "link": 71, "logpdf": 31, "margin": 50, "mcmc": [8, 9, 10], "meta": 81, "meta_model_engin": [], "metamodel": [60, 65, 68, 69, 73, 75, 76, 78, 80], "model": [65, 67, 68, 69, 71, 73, 74, 75, 76, 78, 80, 81], "model1": 75, "multi": 67, "necessari": 81, "nl2_model": 75, "nl4_model": 75, "note": [24, 25, 26, 39, 52, 55, 60], "ohagan": 76, "option": 80, "orthogonal_matching_pursuit": [51, 52, 53], "orthogonalmatchingpursuit": 52, "overview": 77, "packag": [], "paramet": [3, 5, 7, 9, 10, 13, 16, 17, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 47, 52, 55, 58, 60, 61, 62, 63], "pollut": 78, "poly_rec_coeff": 37, "post": 81, "post_process": [11, 12, 13], "postprocess": [13, 79], "prior": [65, 68, 69, 72, 73, 76, 78], "priors1": 75, "probabilist": 81, "process": 81, "pylink": [14, 15, 16, 17, 65, 68, 69, 73, 75, 76, 78], "pylinkforwardmodel": [16, 81], "quickstart": 71, "rais": [13, 29, 62], "refer": [52, 55, 58], "reg_fast_ard": [54, 55, 56], "reg_fast_laplac": [57, 58], "regressionfastard": 55, "regressionfastlaplac": 58, "return": [3, 5, 7, 9, 10, 13, 16, 17, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 42, 47, 49, 52, 55, 58, 60, 61, 62, 63], "see": 71, "sequenti": 81, "set": [64, 65, 68, 69, 73, 75, 76, 78, 81], "space": 72, "subdomain": 32, "surrog": [65, 68, 69, 71, 73, 75, 76, 78, 80, 81], "surrogate_model": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], "tabl": 71, "theori": [], "train": [64, 65, 68, 69, 73, 75, 76, 78, 80, 81], "tutori": 81, "uncertainti": 81, "update_precis": 56, "user": 77, "valid": 71, "vblinearregress": 26, "within_rang": 17}})
\ No newline at end of file
diff --git a/docs/build/html/tutorial.html b/docs/build/html/tutorial.html
index 2cf29844b89f6f00b562f3da8838b7d98a97effb..083bf46bb6b33f719bc6e733066076c5f64f7efc 100644
--- a/docs/build/html/tutorial.html
+++ b/docs/build/html/tutorial.html
@@ -3,7 +3,7 @@
   <head><meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="EXAMPLES" href="examples.html" /><link rel="prev" title="Postprocessing, Bayesian inference and Bayesian model comparison" href="post_description.html" />
+<link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="EXAMPLES" href="examples.html" /><link rel="prev" title="Bayesian inference and multi-model comparison" href="bayes_description.html" />
 
     <!-- Generated with Sphinx 7.3.7 and Furo 2023.09.10 -->
         <title>TUTORIAL - bayesvalidrox 1.0.0 documentation</title>
@@ -166,8 +166,11 @@
   <ul class="current">
 <li class="toctree-l1 has-children"><a class="reference internal" href="packagedescription.html">USER GUIDE</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of USER GUIDE</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="input_description.html">Priors, input space and experimental design</a></li>
+<li class="toctree-l2"><a class="reference internal" href="model_description.html">Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l2"><a class="reference internal" href="al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="post_description.html">Postprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current current-page"><a class="current reference internal" href="#">TUTORIAL</a></li>
@@ -698,14 +701,14 @@ plots of posterior predictions if wanted.</p>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
-          <a class="prev-page" href="post_description.html">
+          <a class="prev-page" href="bayes_description.html">
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
               <div class="page-info">
                 <div class="context">
                   <span>Previous</span>
                 </div>
                 
-                <div class="title">Postprocessing, Bayesian inference and Bayesian model comparison</div>
+                <div class="title">Bayesian inference and multi-model comparison</div>
                 
               </div>
             </a>
diff --git a/docs/website/.gitlab-ci.yml b/docs/website/.gitlab-ci.yml
deleted file mode 100644
index 9c3215ef657cdc812403bc931a1808e3e126a871..0000000000000000000000000000000000000000
--- a/docs/website/.gitlab-ci.yml
+++ /dev/null
@@ -1,25 +0,0 @@
-stages:
-  - build
-  - deploy
-
-jupyter-build:
-  stage: build
-  image: python:slim
-  script:
-    - pip install -U jupyter-book
-    - jupyter-book clean .
-    - jupyter-book build .
-  artifacts:
-    paths:
-      - _build/
-
-pages:
-  stage: deploy
-  image: busybox:latest
-  script:
-    - mv _build/html public
-  artifacts:
-    paths:
-      - public
-  rules:
-    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
diff --git a/docs/website/README.md b/docs/website/README.md
deleted file mode 100644
index 847bc6c603cbe61cf223304d4b10bed74fc58d5b..0000000000000000000000000000000000000000
--- a/docs/website/README.md
+++ /dev/null
@@ -1,98 +0,0 @@
-Example Jupyter Book website using GitLab Pages.
-
-Learn more about GitLab Pages at https://about.gitlab.com/stages-devops-lifecycle/pages/ and the official
-documentation https://docs.gitlab.com/ce/user/project/pages/.
-
----
-
-<!-- START doctoc generated TOC please keep comment here to allow auto update -->
-<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
-**Table of Contents**  *generated with [DocToc](https://github.com/thlorenz/doctoc)*
-
-- [GitLab CI](#gitlab-ci)
-- [Building locally](#building-locally)
-- [GitLab User or Group Pages](#gitlab-user-or-group-pages)
-- [Did you fork this project?](#did-you-fork-this-project)
-- [Troubleshooting](#troubleshooting)
-
-<!-- END doctoc generated TOC please keep comment here to allow auto update -->
-
-## GitLab CI
-
-This project's static Pages are built by [GitLab CI/CD][ci], following the steps
-defined in [`.gitlab-ci.yml`](.gitlab-ci.yml):
-
-```bash
-# Full project: https://gitlab.com/pages/jupyterbook
-
-stages:
-  - build
-  - deploy
-
-jupyter-build:
-  stage: build
-  image: python:slim
-  script:
-    - pip install -U jupyter-book
-    - jupyter-book clean .
-    - jupyter-book build .
-  artifacts:
-    paths:
-      - _build/
-
-pages:
-  stage: deploy
-  image: busybox:latest
-  script:
-    - mv _build/html public
-  artifacts:
-    paths:
-      - public
-  rules:
-    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
-```
-
-## Building locally
-
-To work locally with this project, you'll have to follow the steps below:
-
-1. [Install JupyterLab](https://jupyter.org/install) JupyterLab
-1. [Install Jupyter Book](https://jupyterbook.org/start/overview.html#install-jupyter-book)
-1. Fork, clone or download this project
-1. `cd jupyter-book-example` -- the repository contains already an example of a
-   book's source files, as described at [Jupyter Book > Create your book’s source files](https://jupyterbook.org/start/create.html#create-your-books-source-files)
-1. Work on it... see for example [Jupyter Book > Structure and organize your content](https://jupyterbook.org/basics/organize.html)
-1. Make a commit
-1. See the book published online at 
-
-Read more at Jupyter Book's [Introduction](https://jupyterbook.org/intro.html).
-
-## GitLab User or Group Pages
-
-To use this project as your user/group website, you will need one additional
-step: just rename your project to `namespace.gitlab.io`, where `namespace` is
-your `username` or `groupname`. This can be done by navigating to your
-project's **Settings**.
-
-## Did you fork this project?
-
-If you forked this project for your own use, please go to your project's
-**Settings** and remove the forking relationship, which won't be necessary
-unless you want to contribute back to the upstream project.
-
-## Troubleshooting
-
-1. CSS is missing! That means two things:
-
-    Either that you have wrongly set up the CSS URL in your templates, or
-    your static generator has a configuration option that needs to be explicitly
-    set in order to serve static assets under a relative URL.
-
-[ci]: https://about.gitlab.com/gitlab-ci/
-[jupyter-book]: http://jupyterbook.org
-[install]: https://jupyterbook.org/start/overview.html#install-jupyter-book
-[documentation]: https://jupyterbook.org/intro.html
-
-----
-
-Forked from @NikosAlexandris -- Thank you @stingrayza
diff --git a/docs/website/_config.yml b/docs/website/_config.yml
deleted file mode 100644
index bf20ab421d0e27413be9b7b88e935b2f3dab7b16..0000000000000000000000000000000000000000
--- a/docs/website/_config.yml
+++ /dev/null
@@ -1,52 +0,0 @@
-# Book settings
-# Learn more at https://jupyterbook.org/customize/config.html
-
-title: BayesValidRox
-author: The Jupyter Book Community
-logo: /home/farid/scientific/bayesian-validation/docs/logo/bayesvalidrox-logos.jpeg
-
-# Force re-execution of notebooks on each build.
-# See https://jupyterbook.org/content/execute.html
-execute:
-  execute_notebooks: force
-
-# Define the name of the latex output file for PDF builds
-latex:
-  latex_documents:
-    targetname: book.tex
-
-# Add a bibtex file so that we can create citations
-bibtex_bibfiles:
-  - references.bib
-
-# Information about where the book exists on the web
-repository:
-  url: https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation  # Online location of your book
-  path_to_book: docs  # Optional path to your book, relative to the repository root
-  branch: master  # Which branch of the repository should be used when creating links (optional)
-
-# Add GitHub buttons to your book
-# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
-html:
-  use_issues_button: true
-  use_repository_button: true
-
-# To have “bare” LaTeX rendered in HTML, enable the amsmath extension
-parse:
-  myst_enable_extensions:
-    # don't forget to list any other extensions you want enabled,
-    # including those that are enabled by default!
-    - amsmath
-    - dollarmath
-
-# Manually specify extra files/folders to be included in a website
-sphinx:
-  extra_extensions:
-  - 'sphinx.ext.autodoc'
-  - 'sphinx.ext.autosummary'
-  - 'sphinx.ext.napoleon'
-  - 'sphinx.ext.viewcode'
-  config:
-    autosummary_generate: True
-  config:
-    html_extra_path: ['bayesvalidrox/']
diff --git a/docs/website/_toc.yml b/docs/website/_toc.yml
deleted file mode 100644
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-# Table of contents
-# Learn more at https://jupyterbook.org/customize/toc.html
-
-format: jb-book
-root: intro
-parts:
-  - caption: Name of Part 1
-    chapters:
-    - file: markdown
-    - file: notebooks
-    - file: example_analytical_function
-  - caption: API
-    chapters:
-    - file: index_api
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-API Reference
-=============
-.. autosummary::
-   :toctree: _autosummary
-   :recursive:
-
-   bayesvalidrox
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "13c8e941",
-   "metadata": {},
-   "source": [
-    "# Example: Surrogate Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de58892b",
-   "metadata": {},
-   "source": [
-    "In this example, we train a surrogate model via the `bayesvalidrox` package. We aim at training a Polynomial Chaos Expansion to a simple analytical function. \n",
-    "The PCE representation of the computational model $M$ provides the dependence of this model on the uncertain model's parameters $\\mathbf{\\theta}$ using projection onto an orthonormal polynomial basis. It could be also seen as a linear regression that includes linear combinations of a fixed set of nonlinear functions with respect to the input variables, known as polynomial basis function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2890d7ef",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\label{eq:PCE_Trunc}\n",
-    "    M(x,y,z,t, \\mathbf{\\theta}) \\approx \\sum_{\\mathbf{\\alpha} \\in \\mathcal{A} } c_{\\mathbf{\\alpha}} (x,y,z,t) \\Psi_{\\mathbf{\\alpha}}(\\mathbf{\\theta}) \\, .\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bccdaebc",
-   "metadata": {},
-   "source": [
-    "Here, $x,y,z,t$ are the spatial and temporal components of the quantity of interest, $\\mathbf{\\theta}$ is the vector of the $N$ uncertain parameters of model $M$, $c_{\\mathbf{\\alpha}}(x,y,z,t)  \\in \\mathbb{R}$ are the corresponding expansion coefficients that are functions of space and time, and $\\Psi_{\\mathbf{\\alpha}}(\\mathbf{\\theta})$ represents multivariate polynomials orthogonal with respect to a multi-index $\\mathbf{\\alpha}$. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50023bea",
-   "metadata": {},
-   "source": [
-    "The latter represents the combinatoric information how to enumerate all possible products of $N$ individual univariate basis functions with respect to the total degree of expansions less or equal to polynomial degree $d$:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "538c43c8",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\label{eq:truncation}\n",
-    "\\begin{split}\n",
-    "    \\mathcal{A}^{N, d} = \\{ \\alpha \\in \\mathbb{N}^{N} \\ : \\ |\\alpha|\\leq d\\} \\, , \\qquad\n",
-    "    \\text{card} \\ \\mathcal{A}^{N, d} \\equiv P = \\binom{N+d}{d}.\n",
-    "\\end{split}\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b22c0ccb",
-   "metadata": {},
-   "source": [
-    "The multivariate polynomials $\\Psi_{\\alpha}(\\mathbf{\\theta})$ are comprised of the tensor product of univariate polynomials"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41eeb9d3",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\label{eq:Psi}\n",
-    "    \\Psi_{\\alpha}(\\mathbf{\\theta}_k) :=  \\prod_{i=1}^{N_k} \\psi_{\\alpha_i}^{(i)}(\\mathbf{\\theta}_{k,i}) \\, ,\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "860cbcbd",
-   "metadata": {},
-   "source": [
-    "where the univariate orthonormal polynomials $\\psi_{\\alpha_i}^{(i)}(\\mathbf{\\theta}_{i})$ must satisfy "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "459de9cc",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\label{eq:univPsi}\n",
-    "    \\langle \\psi_j^{(i)}(\\mathbf{\\theta}_{k,i}), \\psi_l^{(i)}(\\mathbf{\\theta}_{k,i}) \\rangle := \\int_{\\Theta_{k,i}} \\psi_j^{(i)}(\\mathbf{\\theta}_{k,i}) \\psi_l^{(i)}(\\mathbf{\\theta}_{k,i}) f_{\\Theta_{k,i}}  (\\mathbf{\\theta}_{k,i})d \\mathbf{\\theta}_{k,i} = \\delta_{j l} \\, .\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34403fc6",
-   "metadata": {},
-   "source": [
-    "Here, $i$ represents the input variable with respect to which the polynomials are orthogonal as well as the corresponding polynomial family, $j$ and $l$ are the corresponding polynomial degree, $f_{\\Theta_{i}}(\\mathbf{\\theta}_{i})$ is the $i$th-input marginal distribution and $\\delta_{j l}$ is the Kronecker delta.\n",
-    "We use an arbitrary polynomial chaos expansion (aPCE), introduced by [Oladyshkin & Nowak (2012)](https://www.sciencedirect.com/science/article/pii/S0951832012000853?casa_token=pbisUgY4niQAAAAA:8WsqMi1mCyfUIJ3GnFGdv6FXFA6a4g8MB75kjGGdEvocV64cd4E8LxcSh8_fwZTeI2ONlUalq_8), that can operate with probability measures that may be implicitly and incompletely defined via their statistical moments. Using aPCE, one can build the multivariate orthonormal polynomials even in the absence of the exact probability density function $f_{\\Theta}(\\theta)$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3bd3feba",
-   "metadata": {},
-   "source": [
-    "In this tutorial, we use an extension of aPCE as Bayesian sparse arbitrary polynomial chaos (BsaPCE) representation. This method computes the coefficients $c_\\alpha$ in a Bayesian setting via a so-called Bayesian sparse learning approach, introduced by [Tipping (2001)](https://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf?ref=https://githubhelp.com)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a291027",
-   "metadata": {},
-   "source": [
-    "The posterior distribution of the expansion coefficients, conditioned on the model responses $\\mathrm{\\mathbf{Y}}$ resulting from the training sets $\\mathbf{X}$, is given by the combination of a Gaussian likelihood and a Gaussian prior distribution over the unknown expansion coefficients $\\mathbf{c}$ according to Bayes' rule. Then, the posterior of the expansion coefficients given the model responses $\\mathrm{\\mathbf{Y}}$ and values of hyper-parameters $\\mathbf{\\alpha}$ and $\\beta$ describing the Gauss process, can take the following form"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3127c49a",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\label{eq:PCE_Posterior}\n",
-    "    p(\\mathrm{\\mathbf{c}}|\\mathbf{Y},\\mathbf{\\alpha}, \\beta) = \\frac{p(\\mathrm{\\mathbf{Y}}|\\mathbf{X},\\mathbf{c}, \\beta) p(\\mathbf{c}|\\mathbf{\\alpha})}{p(\\mathrm{\\mathbf{Y}}| \\mathbf{X}, \\mathbf{\\alpha} , \\beta)},\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46381c6d",
-   "metadata": {},
-   "source": [
-    "which is also Gaussian defined by $\\mathcal{N}( \\mathbf{c}| \\mathbf{\\mu}, \\mathbf{\\Sigma})$ with"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "515d47e6",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\label{eq:PCE_Posterior_moments}\n",
-    "    \\mathbf{\\mu} = \\beta \\mathbf{\\Sigma} \\mathbf{\\Psi}^{\\top} \\mathrm{\\mathbf{Y}} \\, , \\qquad\n",
-    "    \\mathbf{\\Sigma} = \\left(\\mathbf{A}+ \\mathbf{\\Psi}^{\\top} \\beta \\mathbf{\\Psi} \\right)^{-1} \\, .\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce4636d9",
-   "metadata": {},
-   "source": [
-    "Here, $\\mathbf{\\Psi}$ is the design matrix of size $E \\times N$ with elements $\\Psi_{ni}=\\psi_i(x_n)$, where $E$ represents the number of model evaluations using the training samples, and $\\mathbf{A}=\\mathrm{diag}(\\alpha_i)$. The values of $\\mathbf{\\alpha}$ and $\\beta$ can be determined via type-II maximum likelihood [Berger (2013)](https://books.google.com/books?hl=en&lr=&id=1CDaBwAAQBAJ&oi=fnd&pg=PA1&dq=Statistical+decision+theory+and+Bayesian+analysis.++berger+2013&ots=LMulrdTL3O&sig=xMTVRCVf5scWBLQi98BgUie5d-M)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c50a317d",
-   "metadata": {},
-   "source": [
-    "## Problem description: Analytical function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d13b9da3",
-   "metadata": {},
-   "source": [
-    "This test shows a surrogate-assisted Bayesian calibration of a time dependent non-linear analytical function of ten ($n=10$) uncertain parameters $\\omega=\\{\\omega_1, ..., \\omega_n\\}$, which reads as:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b715ea87",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\mathbf{y}(\\boldsymbol{\\omega}, t)=\\left(\\omega_{1}^{2}+\\omega_{2}-1\\right)^{2}+\\omega_{1}^{2}+0.1 \\omega_{1} \\exp \\left(\\omega_{2}\\right)-2 \\omega_{1} \\sqrt{0.5 t}+1+\\sum_{i=2}^{n} \\frac{\\omega_{i}^{3}}{i}\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6625cdf6",
-   "metadata": {},
-   "source": [
-    "where the prior parameter distribution $p(\\omega)$ is considered to be independent and uniform with $\\omega_i \\sim \\mathcal{U} (-5, 5)$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "efe8f59c",
-   "metadata": {},
-   "source": [
-    "## Import necessary libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "31b60d45",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import sys\n",
-    "import joblib\n",
-    "from IPython.display import IFrame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d44d5af",
-   "metadata": {},
-   "source": [
-    "## Define the model with PyLinkForwardModel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf3f4dfe",
-   "metadata": {},
-   "source": [
-    "We use the`PyLinkForwardModel`object for this purpose. Fistly, we are going to import the `bayesvalidrox` package and then, we instantiate the `PyLinkForwardModel` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "25a3b65a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bayesvalidrox import PyLinkForwardModel\n",
-    "Model = PyLinkForwardModel()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0452955f",
-   "metadata": {},
-   "source": [
-    "Next, we will pass the `link_type`, `name` and `py_file` variables to the `Model` object. Since the analytical function is implmented as a python function in a separate file, we only need to pass it's name (without `.py` extension) to the object variable `py_file`. Note that the function name in the python script should match that of the script. For models implemented as a separate python file, the `link_type` is `Function` to be given as a string. The `name` variable takes any user defined string."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f26dbacd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Model.link_type = 'Function'\n",
-    "Model.py_file = 'analytical_function'\n",
-    "Model.name = 'AnalyticFunc'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e12ef2ab",
-   "metadata": {},
-   "source": [
-    "The model output name is defined as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c2778a83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Model.Output.names = ['Z'] # As a list of strings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4106fb6",
-   "metadata": {},
-   "source": [
-    "**Bonus**: For this example, we have a Monte-Carlo reference solution for the first moements (mean and standard deviation) of the analytical function. The numpy (`*.npy`) files can be found in the `data\\` directory. We will discuss the first two moments with our estimate moments using the surrogate model. These values can be passed in a form of a dictionary to the object variable `mc_reference`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9dc9e177",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Model.mc_reference = {}\n",
-    "Model.mc_reference['Time [s]'] = np.arange(0, 10, 1.) / 9\n",
-    "Model.mc_reference['mean'] = np.load(f\"data/mean_2.npy\")\n",
-    "Model.mc_reference['std'] = np.load(f\"data/std_2.npy\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2c68eee",
-   "metadata": {},
-   "source": [
-    "## Define probablistic input model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af7f7d8f",
-   "metadata": {},
-   "source": [
-    "Now, we define the distribution of the model inputs. `bayesvalidrox` accepts the definition in two ways: by defining the distribution directly or by passing available data. The latter is handy, when little information is available on the parameters or they do not follow any typical distributions. We will use the second option and read the input parameters form a numpy file in the `data/` directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f1d2deb0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import and instantiate the input object\n",
-    "from bayesvalidrox import Input\n",
-    "Inputs = Input()\n",
-    "\n",
-    "# Option I: Define distribution directy with their name, type and parameters \n",
-    "\n",
-    "# First parameter\n",
-    "Inputs.addMarginals()\n",
-    "Inputs.Marginals[0].name = '$X_1$'\n",
-    "Inputs.Marginals[0].dist_type = 'unif'\n",
-    "Inputs.Marginals[0].parameters =  [-5, 5]\n",
-    "\n",
-    "# Second parameter\n",
-    "Inputs.addMarginals()\n",
-    "Inputs.Marginals[1].name = '$X_2$'\n",
-    "Inputs.Marginals[1].dist_type = 'unif'\n",
-    "Inputs.Marginals[1].parameters =  [-5, 5]\n",
-    "\n",
-    "# ----------------------------------------------------------------------------\n",
-    "\n",
-    "# Option II: Pass available data for input parameters\n",
-    "# inputParams = np.load('data/InputParameters_2.npy')\n",
-    "\n",
-    "# First parameter\n",
-    "# Inputs.addMarginals()\n",
-    "# Inputs.Marginals[0].name = '$X_1$'\n",
-    "# Inputs.Marginals[0].input_data = inputParams[:, 0]\n",
-    "\n",
-    "# Second parameter\n",
-    "# Inputs.addMarginals()\n",
-    "# Inputs.Marginals[1].name = '$X_2$'\n",
-    "# Inputs.Marginals[1].input_data = inputParams[:, 1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10ff4b23",
-   "metadata": {},
-   "source": [
-    "## Define surrogate (meta) model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d0e8a911",
-   "metadata": {},
-   "source": [
-    "In this example, we use a Polynomial Chaos Expansion (PCE) as our meta model. Like before, we need to import the `MetaModel` object from `bayesvalidrox` package and instantiate a meta-model object. This object, however, accepts the input object (`Input`) as an argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7256d8f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bayesvalidrox import MetaModel\n",
-    "MetaModelOpts = MetaModel(Inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa7756f5",
-   "metadata": {},
-   "source": [
-    "Let us define now the meta model type, the regression type, degree of the polynomials and the trunction norm `q_norm` which lies between 0 and 1. This parameter defines hyperbolic truncation scheme. As for `meta_model_type`, there are two PCE implementations available in `bayesvalidrox`, namely generalized PCE (`PCE`) [Xiu & Karniadakis (2002)](https://doi.org/10.1137/S1064827501387826) or its arbitrary extension (`aPCE`) [Oladyshkin & Nowak (2012)](https://doi.org/10.1016/j.ress.2012.05.002)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "39a2ece3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Select your metamodel method\n",
-    "# Options: PCE and aPCE\n",
-    "MetaModelOpts.meta_model_type = 'aPCE'\n",
-    "\n",
-    "# Select the regression method for calculation of the PCE coefficients:\n",
-    "# 1)OLS: Ordinary Least Square  2)BRR: Bayesian Ridge Regression\n",
-    "# 3)LARS: Least angle regression  4)ARD: Bayesian ARD Regression\n",
-    "# 5)FastARD: Fast Bayesian ARD Regression\n",
-    "# 6)VBL: Variational Bayesian Learning\n",
-    "# 7)EBL: Emperical Bayesian Learning\n",
-    "MetaModelOpts.pce_reg_method = 'FastARD'\n",
-    "\n",
-    "\n",
-    "# Specify the polynomial degree to be compared by the adaptive algorithm:\n",
-    "# The degree with the lowest Leave-One-Out cross-validation (LOO)\n",
-    "# error (or the highest score=1-LOO)estimator is chosen as the final\n",
-    "# metamodel. pce_deg accepts degree as a scalar or a range.\n",
-    "MetaModelOpts.pce_deg = np.arange(9)\n",
-    "\n",
-    "# Hyperbolic truncation scheme 0<q<1 (default=1)\n",
-    "MetaModelOpts.pce_q_norm = 0.75"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3048f38",
-   "metadata": {},
-   "source": [
-    "After defining the metamodel type, we need to define the so-called experimental design (ExpDesign). ExpDesign basically provides instruction on how to samplie the input parameter space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f8e1bf2d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ------------------------------------------------\n",
-    "# ------ Experimental Design Configuration -------\n",
-    "# ------------------------------------------------\n",
-    "MetaModelOpts.add_ExpDesign()\n",
-    "\n",
-    "# One-shot (normal) or Sequential Adaptive (sequential) Design\n",
-    "MetaModelOpts.ExpDesign.Method = 'normal'\n",
-    "MetaModelOpts.ExpDesign.n_init_samples = 100\n",
-    "\n",
-    "# Sampling methods\n",
-    "# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) korobov\n",
-    "# 7) chebyshev(FT) 8) grid(FT) 9) nested_grid(FT) 10)user\n",
-    "MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f7cd8dc",
-   "metadata": {},
-   "source": [
-    "Now, we can start training the surrogate (meta-) model by using the `create_metamodel` method and passing the model object as the only argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "82fffca5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Computing orth. polynomial coeffs: 100%|##########| 2/2 [00:03<00:00,  1.71s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " Now the forward model needs to be run!\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Running forward model : 100%|██████████| 100/100 [00:00<00:00, 7893.97it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      ">>>> Training the aPCE metamodel started. <<<<<<\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fitting regression: 100%|██████████| 1/1 [00:05<00:00,  5.11s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      ">>>> Training the aPCE metamodel sucessfully completed. <<<<<<\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train the meta model\n",
-    "PCEModel = MetaModelOpts.create_metamodel(Model)\n",
-    "\n",
-    "# Save PCE models as pkl object for further deployment for example on a cloud\n",
-    "with open(f'PCEModel_{Model.name}.pkl', 'wb') as output:\n",
-    "    joblib.dump(PCEModel, output, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b2b6a1b",
-   "metadata": {},
-   "source": [
-    "## Post-processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64ea5912",
-   "metadata": {},
-   "source": [
-    "As before, we need to import the `PostProcessing` module of `bayesvalidrox` and instantiate it. Bear in mind that it accepts the meta-model object `PCEModel` as the only argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d51e4195",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bayesvalidrox import PostProcessing\n",
-    "PostPCE = PostProcessing(PCEModel)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51aed43d",
-   "metadata": {},
-   "source": [
-    "### Moment comparison"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "69de7856",
-   "metadata": {},
-   "source": [
-    "Since the reference moments obtained from a Monte-Carlo simulation is available, we only need to call the `plotMoments` method from the PostProcessing object. This method generates a plot and stores it in `Outputs_PostProcessing_calib` directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "96e85fb9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"900\"\n",
-       "            height=\"600\"\n",
-       "            src=\"./Outputs_PostProcessing_calib/Mean_Std_PCE.pdf\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7fe078460ee0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1728x1152 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Compute the moments and compare with the Monte-Carlo reference\n",
-    "PostPCE.plotMoments()\n",
-    "# Show the pdf\n",
-    "IFrame(\"./Outputs_PostProcessing_calib/Mean_Std_PCE.pdf\", width=900, height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6cf5333",
-   "metadata": {},
-   "source": [
-    "### Validation of the metamodel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d506ae2",
-   "metadata": {},
-   "source": [
-    "Let us first visually compare the results of the metamodel and the original model, i.e. `Analyrical Function` for 3 randomly drawn samples for the prior parameter distribution. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "cfd42c02",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Running forward model valid: 100%|██████████| 3/3 [00:00<00:00, 15015.41it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"900\"\n",
-       "            height=\"600\"\n",
-       "            src=\"./Outputs_PostProcessing_calib/Model_vs_PCEModel.pdf\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7fe0dc9e8ac0>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1728x1152 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot to check validation visually.\n",
-    "PostPCE.validMetamodel(nValidSamples=3)\n",
-    "# Show the pdf\n",
-    "IFrame(\"./Outputs_PostProcessing_calib/Model_vs_PCEModel.pdf\", width=900, height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b39e05f",
-   "metadata": {},
-   "source": [
-    "Another way to check the accuracy of the meta model is to use the `accuracyCheckMetaModel` method to show the Root Mean Square Error and the validation error. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e2f83de2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Running forward model validSet: 100%|██████████| 200/200 [00:00<00:00, 8584.59it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      ">>>>> Errors of Z <<<<<\n",
-      "\n",
-      "Index  |  RMSE   |  Validation Error\n",
-      "-----------------------------------\n",
-      "1  |  4.415e-01  |  3.393e-08\n",
-      "2  |  4.415e-01  |  3.399e-08\n",
-      "3  |  4.415e-01  |  3.402e-08\n",
-      "4  |  4.415e-01  |  3.404e-08\n",
-      "5  |  4.415e-01  |  3.406e-08\n",
-      "6  |  4.416e-01  |  3.408e-08\n",
-      "7  |  4.418e-01  |  3.412e-08\n",
-      "8  |  4.458e-01  |  3.475e-08\n",
-      "9  |  4.410e-01  |  3.401e-08\n",
-      "10  |  4.412e-01  |  3.405e-08\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Compute and print RMSE error\n",
-    "PostPCE.accuracyCheckMetaModel(nSamples=200)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef686ae7",
-   "metadata": {},
-   "source": [
-    "### Global sensitivity analysis with Sobol indices"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ffecd15",
-   "metadata": {},
-   "source": [
-    "Here, we analyze how the variability of the model response quantity (`Z`) is affected by the variability of each input variable or combinations thereof. Here, we use the so-called Sobol indices ([Sobol original paper](https://mae.ufl.edu/haftka/eoed/protected/Sobol%20Original%20Paper.pdf)), derived from a variance decomposition of model outputs in terms of contributions of each input parameter or combinations thereof. \n",
-    "Using Sobol decomposition, one can describe the total variance of the model in terms of the sum of the summands' variances. Once the PC representation of the model is available, the expansion coefficients are simply gathered according to the dependency of each basis polynomial, square-summed and normalized"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e95b594",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\label{eq:pce-sobol-1st}\n",
-    "\\begin{array}{l}\n",
-    "S_{i_{1}, \\ldots, i_{s}}=\\frac{\\sum\\limits_{j=1}^{M} \\chi_{j} c_{j}^{2}}{\\sum\\limits_{j=1}^{M} c_{j}^{2}} \\, ,\\qquad\n",
-    "\\chi_{j}=\\left\\{\\begin{array}{ll}\n",
-    "1, & \\text { if } \\alpha_{j}^{k}>0, \\quad \\forall j \\in\\left(i_{1}, \\ldots, i_{s}\\right) \\\\[0.5em]\n",
-    "0, & \\text { if } \\alpha_{j}^{k}=0, \\quad \\exists j \\in\\left(i_{1}, \\ldots, i_{s}\\right)\n",
-    "\\end{array}\\right\\} \\, .\n",
-    "\\end{array}\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a255fc43",
-   "metadata": {},
-   "source": [
-    "Here,  $S_{i_{1}, \\ldots, i_{s}}$ is the Sobol index that indicates what fraction of total variance of the response quantity can be traced back to the joint contributions of the parameters $\\theta_{i_{1}}, \\ldots, \\theta_{i_{s}}.$ The index selection operator $\\chi_{j}$ indicates where the chosen parameters $\\theta$ numbered as $i_{1}, \\ldots, i_{s}$ (i.e., $\\left.\\theta_{i_{1}}, \\ldots, \\theta_{i_{s}}\\right)$ have concurrent contributions to the variance within the overall expansion. Simply put, it selects all polynomial terms with the specified combination $i_{1}, \\ldots, i_{s}$ of model parameters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "025e4a83",
-   "metadata": {},
-   "source": [
-    "A complementing measure for sensitivity analysis is the Sobol Total Index. It expresses the total contribution to the variance of model output due to the uncertainty of an individual parameter $\\theta_j$ in all cross-combinations with other parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9da5d910",
-   "metadata": {},
-   "source": [
-    "\\begin{equation}\n",
-    "\\label{eq:pce-sobol-total}\n",
-    "S_{j}^{T}=\\sum_{\\left\\{i_{1}, \\ldots, i_{s}\\right\\} \\supset j} S_{i_{1}, \\ldots, i_{s}},\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "59413b8c",
-   "metadata": {},
-   "source": [
-    "where $S_{j}^{T}$ is simply a summation of all Sobol indices in which the variable $\\theta_j$ appears as univariate as well as joint influences.\n",
-    "The Total Sobol indices sum to one, if input variables are independent. When dealing with correlated variables, however, this is not the case."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "962bf769",
-   "metadata": {},
-   "source": [
-    "To perform the sensitivity analysis with `bayesvalidrox` package, we need to call the `sobolIndicesPCE` of the `PostProcessing` object. This returns two dictionaries containing the single sobol indices and the total ones. Moreover, it plots the Total Sobol Indices and stores the plots in `pdf` format in `Outputs_PostProcessing_calib` directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "135173d6",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"900\"\n",
-       "            height=\"600\"\n",
-       "            src=\"./Outputs_PostProcessing_calib/Sobol_indices.pdf\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7fe0b57d1340>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1728x1152 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the sobol indices\n",
-    "sobol_cell, total_sobol = PostPCE.sobolIndicesPCE()\n",
-    "# Show the pdf\n",
-    "IFrame(\"./Outputs_PostProcessing_calib/Sobol_indices.pdf\", width=900, height=600)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/website/intro.md b/docs/website/intro.md
deleted file mode 100644
index 3b9b60d2910d4227f0856a64b8e47603fdfa5258..0000000000000000000000000000000000000000
--- a/docs/website/intro.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# Welcome to BayesValidRox Jupyter Book
-
-This is a small sample book to give you a feel for how book content is
-structured.
-
-```{note}
-Here is a note
-```
-
-And here is a code block:
-
-```
-e = mc^2
-```
-
-Check out the content pages bundled with this sample book to see more.
diff --git a/docs/website/markdown.md b/docs/website/markdown.md
deleted file mode 100644
index 1cc9c342008c3c21615b7f0517ea8b99534a8384..0000000000000000000000000000000000000000
--- a/docs/website/markdown.md
+++ /dev/null
@@ -1,125 +0,0 @@
-# Markdown Files
-
-Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or
-in regular markdown files (`.md`), you'll write in the same flavor of markdown
-called **MyST Markdown**.
-
-## What is MyST?
-
-MyST stands for "Markedly Structured Text". It
-is a slight variation on a flavor of markdown called "CommonMark" markdown,
-with small syntax extensions to allow you to write **roles** and **directives**
-in the Sphinx ecosystem.
-
-## What are roles and directives?
-
-Roles and directives are two of the most powerful tools in Jupyter Book. They
-are kind of like functions, but written in a markup language. They both
-serve a similar purpose, but **roles are written in one line**, whereas
-**directives span many lines**. They both accept different kinds of inputs,
-and what they do with those inputs depends on the specific role or directive
-that is being called.
-
-### Using a directive
-
-At its simplest, you can insert a directive into your book's content like so:
-
-````
-```{mydirectivename}
-My directive content
-```
-````
-
-This will only work if a directive with name `mydirectivename` already exists
-(which it doesn't). There are many pre-defined directives associated with
-Jupyter Book. For example, to insert a note box into your content, you can
-use the following directive:
-
-````
-```{note}
-Here is a note
-```
-````
-
-This results in:
-
-```{note}
-Here is a note
-```
-
-In your built book.
-
-For more information on writing directives, see the
-[MyST documentation](https://myst-parser.readthedocs.io/).
-
-
-### Using a role
-
-Roles are very similar to directives, but they are less-complex and written
-entirely on one line. You can insert a role into your book's content with
-this pattern:
-
-```
-Some content {rolename}`and here is my role's content!`
-```
-
-Again, roles will only work if `rolename` is a valid role's name. For example,
-the `doc` role can be used to refer to another page in your book. You can
-refer directly to another page by its relative path. For example, the
-role syntax `` {doc}`intro` `` will result in: {doc}`intro`.
-
-For more information on writing roles, see the
-[MyST documentation](https://myst-parser.readthedocs.io/).
-
-
-### Adding a citation
-
-You can also cite references that are stored in a `bibtex` file. For example,
-the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like
-this: {cite}`holdgraf_evidence_2014`.
-
-Moreoever, you can insert a bibliography into your page with this syntax:
-The `{bibliography}` directive must be used for all the `{cite}` roles to
-render properly.
-For example, if the references for your book are stored in `references.bib`,
-then the bibliography is inserted with:
-
-````
-```{bibliography}
-```
-````
-
-Resulting in a rendered bibliography that looks like:
-
-```{bibliography}
-```
-
-
-### Executing code in your markdown files
-
-If you'd like to include computational content inside these markdown files,
-you can use MyST Markdown to define cells that will be executed when your
-book is built. Jupyter Book uses *jupytext* to do this.
-
-First, add Jupytext metadata to the file. For example, to add Jupytext metadata
-to this markdown page, run this command:
-
-```
-jupyter-book myst init markdown.md
-```
-
-Once a markdown file has Jupytext metadata in it, you can add the following
-directive to run the code at build time:
-
-````
-```{code-cell}
-print("Here is some code to execute")
-```
-````
-
-When your book is built, the contents of any `{code-cell}` blocks will be
-executed with your default Jupyter kernel, and their outputs will be displayed
-in-line with the rest of your content.
-
-For more information about executing computational content with Jupyter Book,
-see [The MyST-NB documentation](https://myst-nb.readthedocs.io/).
diff --git a/docs/website/notebooks.ipynb b/docs/website/notebooks.ipynb
deleted file mode 100644
index 4974f3327359fb623effe551f04a5663a37e9d3b..0000000000000000000000000000000000000000
--- a/docs/website/notebooks.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Content with notebooks\n",
-    "\n",
-    "You can also create content with Jupyter Notebooks. This means that you can include\n",
-    "code blocks and their outputs in your book.\n",
-    "\n",
-    "## Markdown + notebooks\n",
-    "\n",
-    "As it is markdown, you can embed images, HTML, etc into your posts!\n",
-    "\n",
-    "![](https://myst-parser.readthedocs.io/en/latest/_static/logo.png)\n",
-    "\n",
-    "You can also $add_{math}$ and\n",
-    "\n",
-    "$$\n",
-    "math^{blocks}\n",
-    "$$\n",
-    "\n",
-    "or\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mbox{mean} la_{tex} \\\\ \\\\\n",
-    "math blocks\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n",
-    "\n",
-    "## MyST markdown\n",
-    "\n",
-    "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n",
-    "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n",
-    "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n",
-    "\n",
-    "## Code blocks and outputs\n",
-    "\n",
-    "Jupyter Book will also embed your code blocks and output in your book.\n",
-    "For example, here's some sample Matplotlib code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams, cycler\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "plt.ion()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Fixing random state for reproducibility\n",
-    "np.random.seed(19680801)\n",
-    "\n",
-    "N = 10\n",
-    "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n",
-    "data = np.array(data).T\n",
-    "cmap = plt.cm.coolwarm\n",
-    "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n",
-    "\n",
-    "\n",
-    "from matplotlib.lines import Line2D\n",
-    "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n",
-    "                Line2D([0], [0], color=cmap(.5), lw=4),\n",
-    "                Line2D([0], [0], color=cmap(1.), lw=4)]\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 5))\n",
-    "lines = ax.plot(data)\n",
-    "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is a lot more that you can do with outputs (such as including interactive outputs)\n",
-    "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.0"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/website/references.bib b/docs/website/references.bib
deleted file mode 100644
index 783ec6aa5afe2f0a66087d01a112f543e1ed287e..0000000000000000000000000000000000000000
--- a/docs/website/references.bib
+++ /dev/null
@@ -1,56 +0,0 @@
----
----
-
-@inproceedings{holdgraf_evidence_2014,
-	address = {Brisbane, Australia, Australia},
-	title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}},
-	booktitle = {International {Conference} on {Cognitive} {Neuroscience}},
-	publisher = {Frontiers in Neuroscience},
-	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.},
-	year = {2014}
-}
-
-@article{holdgraf_rapid_2016,
-	title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility},
-	volume = {7},
-	issn = {2041-1723},
-	url = {http://www.nature.com/doifinder/10.1038/ncomms13654},
-	doi = {10.1038/ncomms13654},
-	number = {May},
-	journal = {Nature Communications},
-	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.},
-	year = {2016},
-	pages = {13654},
-	file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf}
-}
-
-@inproceedings{holdgraf_portable_2017,
-	title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science},
-	volume = {Part F1287},
-	isbn = {978-1-4503-5272-7},
-	doi = {10.1145/3093338.3093370},
-	abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.},
-	booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}},
-	author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.},
-	year = {2017},
-	keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy}
-}
-
-@article{holdgraf_encoding_2017,
-	title = {Encoding and decoding models in cognitive electrophysiology},
-	volume = {11},
-	issn = {16625137},
-	doi = {10.3389/fnsys.2017.00061},
-	abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.},
-	journal = {Frontiers in Systems Neuroscience},
-	author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.},
-	year = {2017},
-	keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials}
-}
-
-@book{ruby,
-  title     = {The Ruby Programming Language},
-  author    = {Flanagan, David and Matsumoto, Yukihiro},
-  year      = {2008},
-  publisher = {O'Reilly Media}
-}
diff --git a/docs/website/requirements.txt b/docs/website/requirements.txt
deleted file mode 100644
index 7e821e45db31376729c73f3616fb24db2b655a95..0000000000000000000000000000000000000000
--- a/docs/website/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-jupyter-book
-matplotlib
-numpy