From ecf157a76b780d11ede26879288ba27ba5b17c78 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Wed, 26 Jun 2024 16:13:28 +0200
Subject: [PATCH] Added to user guide for training and active learning, and
 added contribution guideline to main page

---
 docs/build/doctrees/al_description.doctree    | Bin 3031 -> 8922 bytes
 docs/build/doctrees/environment.pickle        | Bin 3058311 -> 3081345 bytes
 docs/build/doctrees/index.doctree             | Bin 12519 -> 13835 bytes
 .../doctrees/surrogate_description.doctree    | Bin 21798 -> 24802 bytes
 .../html/_sources/al_description.rst.txt      |  23 +++++++-
 docs/build/html/_sources/index.rst.txt        |  25 ++++----
 .../_sources/surrogate_description.rst.txt    |  20 ++++---
 docs/build/html/al_description.html           |  45 +++++++++++++-
 docs/build/html/index.html                    |  55 ++++--------------
 docs/build/html/searchindex.js                |   2 +-
 docs/build/html/surrogate_description.html    |  17 +++---
 docs/source/al_description.rst                |  23 +++++++-
 docs/source/index.rst                         |  25 ++++----
 docs/source/surrogate_description.rst         |  20 ++++---
 14 files changed, 159 insertions(+), 96 deletions(-)

diff --git a/docs/build/doctrees/al_description.doctree b/docs/build/doctrees/al_description.doctree
index 5f366996787c9ccd381b08ec3663147404becf1a..aff225b68270638d460442acf6078d2bd37fe05b 100644
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diff --git a/docs/build/doctrees/index.doctree b/docs/build/doctrees/index.doctree
index 6274b03111a42b7f4e02e11ea170f4c14f510dd6..7131106e6aa2e77f65e501fc37e5e36e38a49954 100644
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diff --git a/docs/build/doctrees/surrogate_description.doctree b/docs/build/doctrees/surrogate_description.doctree
index 0c4941c5424b03f3bdb35a35f68fb22aa3e7b881..81898fef1fbbe5d6a6b76b051892a12dc74302fc 100644
GIT binary patch
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diff --git a/docs/build/html/_sources/al_description.rst.txt b/docs/build/html/_sources/al_description.rst.txt
index 636fa7877..c1d5e99f2 100644
--- a/docs/build/html/_sources/al_description.rst.txt
+++ b/docs/build/html/_sources/al_description.rst.txt
@@ -1,8 +1,27 @@
 Active learning: iteratively expanding the training set
 *******************************************************
+Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
+The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.
 
-
+.. warning::
+   The active learning methods are currently being reworked. 
+   This should not change the function call ``Engine.train_sequential()``, but will change the associated class structures.
 
 .. image:: ../diagrams/active_learning_reduced.png
    :width: 550
-   :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
\ No newline at end of file
+   :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
+   
+In BayesValidRox AL is realized by additional properties of the :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns` and :any:`bayesvalidrox.surrogate_models.engine.Engine` classes without any changes to the surrogate model.
+
+Exploration, exploitation and tradeoff
+======================================
+**Exploration** methods choose the new samples in a space-filling manner, while **exploitation methods** make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation.
+Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing.
+Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.
+
+The tradeoff between exploration and exploitation is defined by **tradeoff-schemes**, such as an equal split, epsilon-decreaseing or adaptive schemes.
+
+
+Example
+=======
+We take the engine from :any:`surrogate_description` and change the settings to perform sequential training.
diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
index dd9bb06fb..b533a730c 100644
--- a/docs/build/html/_sources/index.rst.txt
+++ b/docs/build/html/_sources/index.rst.txt
@@ -18,7 +18,6 @@ Links
 * PyPI: https://pypi.org/project/bayesvalidrox/
 * Documentation:
 
-#TODO Add other links
 
 Installation
 ------------
@@ -28,35 +27,37 @@ This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0
 
    pip install bayesvalidrox
 
-#TODO Note other needed installations and tips
 
 Quickstart
 ----------
 #TODO A minimal example to get people started
 
-See also
---------
-#TODO Link other websites that are related to BVR
-
 License
 -------
 #TODO Note the License under which BVR is released
 
+Contribution
+------------
+We would be happy for you to contribute to **BayesValidRox**.
+This can include e.g. reporting issues, proposing new features, working on features, or support with the documentation.
+If you want to contibute, check out our contribution_ guidelines.
+You can contact us on the gitlab_ page.
+
+.. _gitlab: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox
+
+.. _contribution: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/docs/sphinx_new/CONTRIBUTING.md?ref_type=heads
+
 Further contents
 ----------------
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    
    packagedescription
    tutorial
    examples
    api
-   
-Contact
--------
-#TODO Add options here
-   
+
    
 Indices and tables
 ==================
diff --git a/docs/build/html/_sources/surrogate_description.rst.txt b/docs/build/html/_sources/surrogate_description.rst.txt
index 6cd7bcd29..61d89e6a6 100644
--- a/docs/build/html/_sources/surrogate_description.rst.txt
+++ b/docs/build/html/_sources/surrogate_description.rst.txt
@@ -2,16 +2,9 @@ Training surrogate models
 *************************
 Surrogate models, also called metamodels, are models that are built on evaluations of full models with the goal to capture the full behaviour, but reduce the cost of evaluations.
 
-In BayesValidRox two types of surrogate model are available, polynomial chaos expansion and Gaussian processes.
-The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
-Gaussian processes (GP) give kernel-based representations of the model results.
-
 The surrogate models are trained on datasets :math:`\mathcal{D}=(x_i, y_i)_{i=1,\dots,M)` that consist of :math:`M` samples of the uncertain parameters and the corresponding model outputs.
 We call this dataset the training data, with training samples :math:`(x_i)_{i=1,\dots,M)`.
 
-
-MetaModel and Engine classes
-============================
 BayesValidRox creates surrogate models as objects of the class :any:`bayesvalidrox.surrogate_models.surrogate_models.MetaModel`.
 Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.Engine`.
 
@@ -21,6 +14,19 @@ Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.E
    
 MetaModel options
 =================
+In BayesValidRox two types of surrogate model are available, Polynomial Chaos Expansion (PCE) and Gaussian Processes (GP).
+The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
+Gaussian processes (GP) give kernel-based representations of the model results.
+
+We provide a broad range of regression methods for useage with PCE-surrogates that can be set by the parameter ``MetaModel.pce_reg_method``.
+These include Ordinary Least Squares (``ols``), Bayesian Ridge Regression (``brr``), Least angle regression (``lars``), Bayesian ARD Regression (``ard``), Fast Bayesian ARD Regression (``fastard``), Variational Bayesian Learning (``vbl``) and Emperical Bayesian Learning (``ebl``).
+Depending on the chosen regression method, the surrogate outputs a mean approximation and an associated standard deviation.
+
+Dimensionality reduction can be performed on outputs with Principal Component Analysis (PCA).
+PCA is applied on the set of surrogates built for the ``x_values`` defined in the model.
+
+If bootstrapping is used, multiple surrogates will be created based on bootstrapped training data, and jointly evaluated.
+The final outputs will then be the mean and standard deviation of their approximations.
 
 
 Training with the engine
diff --git a/docs/build/html/al_description.html b/docs/build/html/al_description.html
index aa3d9fc17..d00aea23b 100644
--- a/docs/build/html/al_description.html
+++ b/docs/build/html/al_description.html
@@ -141,7 +141,7 @@
           <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
         </button>
       </div>
-      <label class="toc-overlay-icon toc-header-icon no-toc" for="__toc">
+      <label class="toc-overlay-icon toc-header-icon" for="__toc">
         <div class="visually-hidden">Toggle table of contents sidebar</div>
         <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
       </label>
@@ -329,7 +329,7 @@
               <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
             </button>
           </div>
-          <label class="toc-overlay-icon toc-content-icon no-toc" for="__toc">
+          <label class="toc-overlay-icon toc-content-icon" for="__toc">
             <div class="visually-hidden">Toggle table of contents sidebar</div>
             <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
           </label>
@@ -337,7 +337,26 @@
         <article role="main">
           <section id="active-learning-iteratively-expanding-the-training-set">
 <h1>Active learning: iteratively expanding the training set<a class="headerlink" href="#active-learning-iteratively-expanding-the-training-set" title="Link to this heading">¶</a></h1>
+<p>Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
+The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The active learning methods are currently being reworked.
+This should not change the function call <code class="docutils literal notranslate"><span class="pre">Engine.train_sequential()</span></code>, but will change the associated class structures.</p>
+</div>
 <a class="reference internal image-reference" href="_images/active_learning_reduced.png"><img alt="UML diagram for the classes and functions used in active learning in BayesValidRox." src="_images/active_learning_reduced.png" style="width: 550px;" /></a>
+<p>In BayesValidRox AL is realized by additional properties of the <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</span></code></a> and <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a> classes without any changes to the surrogate model.</p>
+<section id="exploration-exploitation-and-tradeoff">
+<h2>Exploration, exploitation and tradeoff<a class="headerlink" href="#exploration-exploitation-and-tradeoff" title="Link to this heading">¶</a></h2>
+<p><strong>Exploration</strong> methods choose the new samples in a space-filling manner, while <strong>exploitation methods</strong> make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation.
+Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing.
+Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.</p>
+<p>The tradeoff between exploration and exploitation is defined by <strong>tradeoff-schemes</strong>, such as an equal split, epsilon-decreaseing or adaptive schemes.</p>
+</section>
+<section id="example">
+<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
+<p>We take the engine from <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a> and change the settings to perform sequential training.</p>
+</section>
 </section>
 
         </article>
@@ -383,9 +402,29 @@
         
       </footer>
     </div>
-    <aside class="toc-drawer no-toc">
+    <aside class="toc-drawer">
       
       
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Active learning: iteratively expanding the training set</a><ul>
+<li><a class="reference internal" href="#exploration-exploitation-and-tradeoff">Exploration, exploitation and tradeoff</a></li>
+<li><a class="reference internal" href="#example">Example</a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
       
     </aside>
   </div>
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index 9fd0f8c02..ac12863ce 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -348,7 +348,6 @@ The functionality and options for the different classes is described more in-dep
 <li><p>PyPI: <a class="reference external" href="https://pypi.org/project/bayesvalidrox/">https://pypi.org/project/bayesvalidrox/</a></p></li>
 <li><p>Documentation:</p></li>
 </ul>
-<p>#TODO Add other links</p>
 </section>
 <section id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
@@ -356,62 +355,33 @@ The functionality and options for the different classes is described more in-dep
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>bayesvalidrox
 </pre></div>
 </div>
-<p>#TODO Note other needed installations and tips</p>
 </section>
 <section id="quickstart">
 <h2>Quickstart<a class="headerlink" href="#quickstart" title="Link to this heading">¶</a></h2>
 <p>#TODO A minimal example to get people started</p>
 </section>
-<section id="see-also">
-<h2>See also<a class="headerlink" href="#see-also" title="Link to this heading">¶</a></h2>
-<p>#TODO Link other websites that are related to BVR</p>
-</section>
 <section id="license">
 <h2>License<a class="headerlink" href="#license" title="Link to this heading">¶</a></h2>
 <p>#TODO Note the License under which BVR is released</p>
 </section>
+<section id="contribution">
+<h2>Contribution<a class="headerlink" href="#contribution" title="Link to this heading">¶</a></h2>
+<p>We would be happy for you to contribute to <strong>BayesValidRox</strong>.
+This can include e.g. reporting issues, proposing new features, working on features, or support with the documentation.
+If you want to contibute, check out our <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/docs/sphinx_new/CONTRIBUTING.md?ref_type=heads">contribution</a> guidelines.
+You can contact us on the <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox">gitlab</a> page.</p>
+</section>
 <section id="further-contents">
 <h2>Further contents<a class="headerlink" href="#further-contents" title="Link to this heading">¶</a></h2>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">USER GUIDE</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="packagedescription.html#installation">Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="tutorial.html">TUTORIAL</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#import-necessary-libraries">Import necessary libraries</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#define-the-model-with-pylinkforwardmodel">Define the model with PyLinkForwardModel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#define-probabilistic-input-model">Define probabilistic input model</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#define-surrogate-meta-model">Define surrogate (meta) model</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#set-the-experimental-design">Set the experimental design</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#train-the-surrogate-with-an-engine">Train the surrogate with an engine</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#sequential-training">Sequential training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#post-processing">Post-processing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#bayesian-inference">Bayesian Inference</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#define-the-data-uncertainty">Define the data uncertainty</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="examples.html">EXAMPLES</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="analyticalfunction.html">Analytical function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="beam.html">Beam</a></li>
-<li class="toctree-l2"><a class="reference internal" href="borehole.html">Borehole</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ishigami.html">Ishigami</a></li>
-<li class="toctree-l2"><a class="reference internal" href="modelcomparison.html">Model comparison</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ohaganfunction.html">OHagan-function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pollution.html">Pollution</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="api.html">API</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">USER GUIDE</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorial.html">TUTORIAL</a></li>
+<li class="toctree-l1"><a class="reference internal" href="examples.html">EXAMPLES</a></li>
+<li class="toctree-l1"><a class="reference internal" href="api.html">API</a></li>
 </ul>
 </div>
 </section>
-<section id="contact">
-<h2>Contact<a class="headerlink" href="#contact" title="Link to this heading">¶</a></h2>
-<p>#TODO Add options here</p>
-</section>
 </section>
 <section id="indices-and-tables">
 <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading">¶</a></h1>
@@ -471,10 +441,9 @@ The functionality and options for the different classes is described more in-dep
 <li><a class="reference internal" href="#links">Links</a></li>
 <li><a class="reference internal" href="#installation">Installation</a></li>
 <li><a class="reference internal" href="#quickstart">Quickstart</a></li>
-<li><a class="reference internal" href="#see-also">See also</a></li>
 <li><a class="reference internal" href="#license">License</a></li>
+<li><a class="reference internal" href="#contribution">Contribution</a></li>
 <li><a class="reference internal" href="#further-contents">Further contents</a></li>
-<li><a class="reference internal" href="#contact">Contact</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index fba7b51c0..649219d8b 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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"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", 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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
+Search.setIndex({"alltitles": {"API": [[66, "api"]], "Active learning: iteratively expanding the training set": [[64, "active-learning-iteratively-expanding-the-training-set"]], "Arguments": [[29, "arguments"], [42, "arguments"], [42, "id2"]], "Attributes": [[3, "attributes"], [5, "attributes"], [7, "attributes"], [9, "attributes"], [13, "attributes"], [16, "attributes"], [25, "attributes"], [26, "attributes"], [39, "attributes"], [42, "attributes"], [47, "attributes"], [49, "attributes"], [50, "attributes"], [52, "attributes"], [55, "attributes"], [58, "attributes"], [60, "attributes"]], "Bayesian Inference": [[81, "bayesian-inference"]], "Bayesian inference and multi-model comparison": [[67, "bayesian-inference-and-multi-model-comparison"]], "Contribution": [[71, "contribution"]], "Define probabilistic input model": [[81, "define-probabilistic-input-model"]], "Define surrogate (meta) model": [[81, "define-surrogate-meta-model"]], "Define the data uncertainty": [[81, 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"Training surrogate models": [[80, "training-surrogate-models"]], "Training with the engine": [[80, "training-with-the-engine"]], "USER GUIDE": [[77, "user-guide"]], "bayesvalidrox": [[0, "module-bayesvalidrox"]], "bayesvalidrox.bayes_inference": [[1, "module-bayesvalidrox.bayes_inference"]], "bayesvalidrox.bayes_inference.bayes_inference": [[2, "module-bayesvalidrox.bayes_inference.bayes_inference"]], "bayesvalidrox.bayes_inference.bayes_inference.BayesInference": [[3, "bayesvalidrox-bayes-inference-bayes-inference-bayesinference"]], "bayesvalidrox.bayes_inference.bayes_model_comparison": [[4, "module-bayesvalidrox.bayes_inference.bayes_model_comparison"]], "bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison": [[5, "bayesvalidrox-bayes-inference-bayes-model-comparison-bayesmodelcomparison"]], "bayesvalidrox.bayes_inference.discrepancy": [[6, "module-bayesvalidrox.bayes_inference.discrepancy"]], "bayesvalidrox.bayes_inference.discrepancy.Discrepancy": [[7, 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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", 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\ No newline at end of file
diff --git a/docs/build/html/surrogate_description.html b/docs/build/html/surrogate_description.html
index cbaf39a07..a9af86955 100644
--- a/docs/build/html/surrogate_description.html
+++ b/docs/build/html/surrogate_description.html
@@ -338,19 +338,23 @@
           <section id="training-surrogate-models">
 <h1>Training surrogate models<a class="headerlink" href="#training-surrogate-models" title="Link to this heading">¶</a></h1>
 <p>Surrogate models, also called metamodels, are models that are built on evaluations of full models with the goal to capture the full behaviour, but reduce the cost of evaluations.</p>
-<p>In BayesValidRox two types of surrogate model are available, polynomial chaos expansion and Gaussian processes.
-The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
-Gaussian processes (GP) give kernel-based representations of the model results.</p>
 <p>The surrogate models are trained on datasets <span class="math notranslate nohighlight">\(\mathcal{D}=(x_i, y_i)_{i=1,\dots,M)\)</span> that consist of <span class="math notranslate nohighlight">\(M\)</span> samples of the uncertain parameters and the corresponding model outputs.
 We call this dataset the training data, with training samples <span class="math notranslate nohighlight">\((x_i)_{i=1,\dots,M)\)</span>.</p>
-<section id="metamodel-and-engine-classes">
-<h2>MetaModel and Engine classes<a class="headerlink" href="#metamodel-and-engine-classes" title="Link to this heading">¶</a></h2>
 <p>BayesValidRox creates surrogate models as objects of the class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</span></code></a>.
 Training is performed by the class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a>.</p>
 <a class="reference internal image-reference" href="_images/metamod_training_reduced.png"><img alt="UML diagram for metamodel-related classes in bayesvalidrox" src="_images/metamod_training_reduced.png" style="width: 800px;" /></a>
-</section>
 <section id="metamodel-options">
 <h2>MetaModel options<a class="headerlink" href="#metamodel-options" title="Link to this heading">¶</a></h2>
+<p>In BayesValidRox two types of surrogate model are available, Polynomial Chaos Expansion (PCE) and Gaussian Processes (GP).
+The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
+Gaussian processes (GP) give kernel-based representations of the model results.</p>
+<p>We provide a broad range of regression methods for useage with PCE-surrogates that can be set by the parameter <code class="docutils literal notranslate"><span class="pre">MetaModel.pce_reg_method</span></code>.
+These include Ordinary Least Squares (<code class="docutils literal notranslate"><span class="pre">ols</span></code>), Bayesian Ridge Regression (<code class="docutils literal notranslate"><span class="pre">brr</span></code>), Least angle regression (<code class="docutils literal notranslate"><span class="pre">lars</span></code>), Bayesian ARD Regression (<code class="docutils literal notranslate"><span class="pre">ard</span></code>), Fast Bayesian ARD Regression (<code class="docutils literal notranslate"><span class="pre">fastard</span></code>), Variational Bayesian Learning (<code class="docutils literal notranslate"><span class="pre">vbl</span></code>) and Emperical Bayesian Learning (<code class="docutils literal notranslate"><span class="pre">ebl</span></code>).
+Depending on the chosen regression method, the surrogate outputs a mean approximation and an associated standard deviation.</p>
+<p>Dimensionality reduction can be performed on outputs with Principal Component Analysis (PCA).
+PCA is applied on the set of surrogates built for the <code class="docutils literal notranslate"><span class="pre">x_values</span></code> defined in the model.</p>
+<p>If bootstrapping is used, multiple surrogates will be created based on bootstrapped training data, and jointly evaluated.
+The final outputs will then be the mean and standard deviation of their approximations.</p>
 </section>
 <section id="training-with-the-engine">
 <h2>Training with the engine<a class="headerlink" href="#training-with-the-engine" title="Link to this heading">¶</a></h2>
@@ -491,7 +495,6 @@ Evaluation via the surrogate model can make use of the sampling in the experimen
           <div class="toc-tree">
             <ul>
 <li><a class="reference internal" href="#">Training surrogate models</a><ul>
-<li><a class="reference internal" href="#metamodel-and-engine-classes">MetaModel and Engine classes</a></li>
 <li><a class="reference internal" href="#metamodel-options">MetaModel options</a></li>
 <li><a class="reference internal" href="#training-with-the-engine">Training with the engine</a></li>
 <li><a class="reference internal" href="#example">Example</a></li>
diff --git a/docs/source/al_description.rst b/docs/source/al_description.rst
index 636fa7877..c1d5e99f2 100644
--- a/docs/source/al_description.rst
+++ b/docs/source/al_description.rst
@@ -1,8 +1,27 @@
 Active learning: iteratively expanding the training set
 *******************************************************
+Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
+The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.
 
-
+.. warning::
+   The active learning methods are currently being reworked. 
+   This should not change the function call ``Engine.train_sequential()``, but will change the associated class structures.
 
 .. image:: ../diagrams/active_learning_reduced.png
    :width: 550
-   :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
\ No newline at end of file
+   :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
+   
+In BayesValidRox AL is realized by additional properties of the :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns` and :any:`bayesvalidrox.surrogate_models.engine.Engine` classes without any changes to the surrogate model.
+
+Exploration, exploitation and tradeoff
+======================================
+**Exploration** methods choose the new samples in a space-filling manner, while **exploitation methods** make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation.
+Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing.
+Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.
+
+The tradeoff between exploration and exploitation is defined by **tradeoff-schemes**, such as an equal split, epsilon-decreaseing or adaptive schemes.
+
+
+Example
+=======
+We take the engine from :any:`surrogate_description` and change the settings to perform sequential training.
diff --git a/docs/source/index.rst b/docs/source/index.rst
index dd9bb06fb..b533a730c 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -18,7 +18,6 @@ Links
 * PyPI: https://pypi.org/project/bayesvalidrox/
 * Documentation:
 
-#TODO Add other links
 
 Installation
 ------------
@@ -28,35 +27,37 @@ This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0
 
    pip install bayesvalidrox
 
-#TODO Note other needed installations and tips
 
 Quickstart
 ----------
 #TODO A minimal example to get people started
 
-See also
---------
-#TODO Link other websites that are related to BVR
-
 License
 -------
 #TODO Note the License under which BVR is released
 
+Contribution
+------------
+We would be happy for you to contribute to **BayesValidRox**.
+This can include e.g. reporting issues, proposing new features, working on features, or support with the documentation.
+If you want to contibute, check out our contribution_ guidelines.
+You can contact us on the gitlab_ page.
+
+.. _gitlab: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox
+
+.. _contribution: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/docs/sphinx_new/CONTRIBUTING.md?ref_type=heads
+
 Further contents
 ----------------
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    
    packagedescription
    tutorial
    examples
    api
-   
-Contact
--------
-#TODO Add options here
-   
+
    
 Indices and tables
 ==================
diff --git a/docs/source/surrogate_description.rst b/docs/source/surrogate_description.rst
index 6cd7bcd29..61d89e6a6 100644
--- a/docs/source/surrogate_description.rst
+++ b/docs/source/surrogate_description.rst
@@ -2,16 +2,9 @@ Training surrogate models
 *************************
 Surrogate models, also called metamodels, are models that are built on evaluations of full models with the goal to capture the full behaviour, but reduce the cost of evaluations.
 
-In BayesValidRox two types of surrogate model are available, polynomial chaos expansion and Gaussian processes.
-The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
-Gaussian processes (GP) give kernel-based representations of the model results.
-
 The surrogate models are trained on datasets :math:`\mathcal{D}=(x_i, y_i)_{i=1,\dots,M)` that consist of :math:`M` samples of the uncertain parameters and the corresponding model outputs.
 We call this dataset the training data, with training samples :math:`(x_i)_{i=1,\dots,M)`.
 
-
-MetaModel and Engine classes
-============================
 BayesValidRox creates surrogate models as objects of the class :any:`bayesvalidrox.surrogate_models.surrogate_models.MetaModel`.
 Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.Engine`.
 
@@ -21,6 +14,19 @@ Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.E
    
 MetaModel options
 =================
+In BayesValidRox two types of surrogate model are available, Polynomial Chaos Expansion (PCE) and Gaussian Processes (GP).
+The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
+Gaussian processes (GP) give kernel-based representations of the model results.
+
+We provide a broad range of regression methods for useage with PCE-surrogates that can be set by the parameter ``MetaModel.pce_reg_method``.
+These include Ordinary Least Squares (``ols``), Bayesian Ridge Regression (``brr``), Least angle regression (``lars``), Bayesian ARD Regression (``ard``), Fast Bayesian ARD Regression (``fastard``), Variational Bayesian Learning (``vbl``) and Emperical Bayesian Learning (``ebl``).
+Depending on the chosen regression method, the surrogate outputs a mean approximation and an associated standard deviation.
+
+Dimensionality reduction can be performed on outputs with Principal Component Analysis (PCA).
+PCA is applied on the set of surrogates built for the ``x_values`` defined in the model.
+
+If bootstrapping is used, multiple surrogates will be created based on bootstrapped training data, and jointly evaluated.
+The final outputs will then be the mean and standard deviation of their approximations.
 
 
 Training with the engine
-- 
GitLab