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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
index dfe713a83e3a8296fa582b55e78564ab15b200e8..7b1fdb68977338e132a6a9856e5584e1a83df803 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html
@@ -10,7 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=362ab14a" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=da48c412" />
+    <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=3c2b257a" />
     
     
 
@@ -164,8 +164,14 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <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="../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>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -175,12 +181,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../packagedescription.html">PACKAGE DESCRIPTION</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of PACKAGE DESCRIPTION</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="../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>
-</ul>
-</li>
 <li class="toctree-l1 current has-children"><a class="reference internal" href="../api.html">API</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l2 current has-children"><a class="reference internal" href="bayesvalidrox.html">bayesvalidrox</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l3 has-children"><a class="reference internal" href="bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
@@ -336,7 +336,7 @@
 <h1>bayesvalidrox.surrogate_models.exp_designs.ExpDesigns<a class="headerlink" href="#bayesvalidrox-surrogate-models-exp-designs-expdesigns" title="Link to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.</span></span><span class="sig-name descname"><span class="pre">ExpDesigns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_Model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pce'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_new_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod_LOO_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tradeoff_scheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_canddidate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explore_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">util_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cand_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_replication</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_a_post</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adapt_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_func_itr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.</span></span><span class="sig-name descname"><span class="pre">ExpDesigns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_Model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pce'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_new_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod_LOO_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tradeoff_scheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_canddidate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explore_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">util_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cand_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_replication</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_a_post</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adapt_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_func_itr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="Link to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace" title="bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="xref py py-class docutils literal notranslate"><span class="pre">InputSpace</span></code></a></p>
 <p>This class generates samples from the prescribed marginals for the model
 parameters using the <cite>Input</cite> object.</p>
@@ -435,7 +435,7 @@ supported:</p>
 - K-Opt (K-Optimality)</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_Model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pce'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_new_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod_LOO_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tradeoff_scheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_canddidate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explore_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">util_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cand_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_replication</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_a_post</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adapt_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_func_itr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_Model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pce'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_new_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod_LOO_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tradeoff_scheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_canddidate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explore_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">util_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cand_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_replication</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_a_post</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adapt_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_func_itr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__" title="Link to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <p class="rubric">Methods</p>
@@ -463,13 +463,16 @@ supported:</p>
 <tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pcm_sampler</span></code></a>(n_samples, max_deg)</p></td>
 <td><p>Generates collocation points based on the root of the polynomial degrees.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_sampler</span></code></a>(n_samples[, max_deg])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.plot_samples" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.plot_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_samples</span></code></a>(samples)</p></td>
+<td><p>Visualizes generated samples over their given distributions.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_sampler</span></code></a>(n_samples[, max_deg])</p></td>
 <td><p>Samples the given raw data randomly.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read_from_file</span></code></a>(out_names)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read_from_file</span></code></a>(out_names)</p></td>
 <td><p>Reads in the ExpDesign from a provided h5py file and saves the results.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X[, params, method])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X[, params, method])</p></td>
 <td><p>Transforms the samples via either a Rosenblatt or an isoprobabilistic transformation.</p></td>
 </tr>
 </tbody>
@@ -513,14 +516,14 @@ the MetaModel object.</p>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED">
-<span class="sig-name descname"><span class="pre">generate_ED</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pce_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">generate_ED</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pce_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED" title="Link to this definition">¶</a></dt>
 <dd><p>Generates experimental designs (training set) with the given method.</p>
 <section id="id2">
 <h3>Parameters<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>n_samples<span class="classifier">int</span></dt><dd><p>Number of requested training points.</p>
 </dd>
-<dt>max_pce_deg<span class="classifier">int, optional</span></dt><dd><p>Maximum PCE polynomial degree. The default is <cite>None</cite>.</p>
+<dt>max_pce_deg<span class="classifier">int, optional</span></dt><dd><p>Maximum PCE polynomial degree. The default is 1.</p>
 </dd>
 </dl>
 </section>
@@ -599,22 +602,39 @@ init_param_space if that has not been done beforehand.</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler">
-<span class="sig-name descname"><span class="pre">random_sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler" title="Link to this definition">¶</a></dt>
-<dd><p>Samples the given raw data randomly.</p>
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.plot_samples">
+<span class="sig-name descname"><span class="pre">plot_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.plot_samples" title="Link to this definition">¶</a></dt>
+<dd><p>Visualizes generated samples over their given distributions.</p>
 <section id="id10">
 <h3>Parameters<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
 <dl class="simple">
+<dt>samples<span class="classifier">array</span></dt><dd><p>The samples to visualize.</p>
+</dd>
+</dl>
+</section>
+<section id="id11">
+<h3>Returns<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler">
+<span class="sig-name descname"><span class="pre">random_sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler" title="Link to this definition">¶</a></dt>
+<dd><p>Samples the given raw data randomly.</p>
+<section id="id12">
+<h3>Parameters<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<dl class="simple">
 <dt>n_samples<span class="classifier">int</span></dt><dd><p>Number of requested samples.</p>
 </dd>
-<dt>max_deg<span class="classifier">int, optional</span></dt><dd><p>Maximum degree. The default is <cite>None</cite>.
+<dt>max_deg<span class="classifier">int, optional</span></dt><dd><p>Maximum degree. The default is 1.
 This will be used to run init_param_space, if it has not been done
 until now.</p>
 </dd>
 </dl>
 </section>
-<section id="id11">
-<h3>Returns<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<section id="id13">
+<h3>Returns<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>samples: array of shape (n_samples, n_params)</dt><dd><p>The sampling locations in the input space.</p>
 </dd>
@@ -626,15 +646,15 @@ until now.</p>
 <dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file">
 <span class="sig-name descname"><span class="pre">read_from_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_names</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file" title="Link to this definition">¶</a></dt>
 <dd><p>Reads in the ExpDesign from a provided h5py file and saves the results.</p>
-<section id="id12">
-<h3>Parameters<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<section id="id14">
+<h3>Parameters<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>out_names<span class="classifier">list of strings</span></dt><dd><p>The keys that are in the outputs (y) saved in the provided file.</p>
 </dd>
 </dl>
 </section>
-<section id="id13">
-<h3>Returns<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<section id="id15">
+<h3>Returns<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
 <p>None.</p>
 </section>
 </dd></dl>
@@ -644,8 +664,8 @@ until now.</p>
 <span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transforms the samples via either a Rosenblatt or an isoprobabilistic
 transformation.</p>
-<section id="id14">
-<h3>Parameters<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<section id="id16">
+<h3>Parameters<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>X<span class="classifier">array of shape (n_samples,n_params)</span></dt><dd><p>Samples to be transformed.</p>
 </dd>
@@ -655,8 +675,8 @@ transformation.</p>
 </dd>
 </dl>
 </section>
-<section id="id15">
-<h3>Returns<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
+<section id="id17">
+<h3>Returns<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
 <dl class="simple">
 <dt>tr_X: array of shape (n_samples,n_params)</dt><dd><p>Transformed samples.</p>
 </dd>
@@ -733,6 +753,7 @@ transformation.</p>
 <li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_samples"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.generate_samples()</span></code></a></li>
 <li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.init_param_space"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.init_param_space()</span></code></a></li>
 <li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.pcm_sampler()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.plot_samples"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.plot_samples()</span></code></a></li>
 <li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.random_sampler()</span></code></a></li>
 <li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.read_from_file()</span></code></a></li>
 <li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.transform()</span></code></a></li>
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 52b6057f07801c1dd2ccd842e9b8cb7ed29096b8..f21e500d43949d3d25f7d89322d7fe38ce8fa249 100644
--- 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
@@ -10,7 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=362ab14a" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=da48c412" />
+    <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=3c2b257a" />
     
     
 
@@ -164,8 +164,14 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <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="../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>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -175,12 +181,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../packagedescription.html">PACKAGE DESCRIPTION</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of PACKAGE DESCRIPTION</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="../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>
-</ul>
-</li>
 <li class="toctree-l1 current has-children"><a class="reference internal" href="../api.html">API</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l2 current has-children"><a class="reference internal" href="bayesvalidrox.html">bayesvalidrox</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l3 has-children"><a class="reference internal" href="bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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 fff3625e7426baa67f438148ad42dafd49244a8a..9de0b16b2bd5ef9799288325403b0a947dec0a4d 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.exp_designs.html
@@ -10,7 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=362ab14a" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=da48c412" />
+    <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=3c2b257a" />
     
     
 
@@ -164,8 +164,14 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <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="../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>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -175,12 +181,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../packagedescription.html">PACKAGE DESCRIPTION</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of PACKAGE DESCRIPTION</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="../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>
-</ul>
-</li>
 <li class="toctree-l1 current has-children"><a class="reference internal" href="../api.html">API</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l2 current has-children"><a class="reference internal" href="bayesvalidrox.html">bayesvalidrox</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l3 has-children"><a class="reference internal" href="bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.html
index 260779090957700b92516c99630ee2fa48cab804..b7b7a8357254d992c619855acd6eb68251b44dac 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.html
@@ -10,7 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=362ab14a" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
-    <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=da48c412" />
+    <link rel="stylesheet" type="text/css" href="../_static/custom.css?v=3c2b257a" />
     
     
 
@@ -164,8 +164,16 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <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="../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, Bayesian inference and Bayesian model comparison</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorial.html">TUTORIAL</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -175,12 +183,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="../packagedescription.html">PACKAGE DESCRIPTION</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of PACKAGE DESCRIPTION</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="../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>
-</ul>
-</li>
 <li class="toctree-l1 current has-children"><a class="reference internal" href="../api.html">API</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l2 current has-children"><a class="reference internal" href="bayesvalidrox.html">bayesvalidrox</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
 <li class="toctree-l3 has-children"><a class="reference internal" href="bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
@@ -438,6 +440,6 @@
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/scripts/furo.js?v=32e29ea5"></script>
-    <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script async="async" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt
index 87376d6c45519e53f1323a61d30def9c57adb745..07efe980f7e7869f5b1c240585d44113c5c0765f 100644
--- a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.exp\_designs.ExpDesigns
+bayesvalidrox.surrogate\_models.exp\_designs.ExpDesigns
 =======================================================
 
 .. currentmodule:: bayesvalidrox.surrogate_models.exp_designs
@@ -23,6 +23,7 @@ bayesvalidrox.surrogate\_models.exp\_designs.ExpDesigns
       ~ExpDesigns.generate_samples
       ~ExpDesigns.init_param_space
       ~ExpDesigns.pcm_sampler
+      ~ExpDesigns.plot_samples
       ~ExpDesigns.random_sampler
       ~ExpDesigns.read_from_file
       ~ExpDesigns.transform
diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
index 028757fe8f8d768052679694c90236ebd9b63be6..48215bbbc3e3d0e4d55b5cb9ee3b007467332885 100644
--- a/docs/build/html/_sources/index.rst.txt
+++ b/docs/build/html/_sources/index.rst.txt
@@ -23,7 +23,10 @@ Links
 Installation
 ------------
 This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on, use pip to install:
- $ pip install bayesvalidrox
+
+.. code-block:: bash
+
+   pip install bayesvalidrox
 
 #TODO Note other needed installations and tips
 
@@ -45,9 +48,9 @@ Further contents
 .. toctree::
    :maxdepth: 2
    
+   packagedescription
    tutorial
    examples
-   packagedescription
    api
    
    
diff --git a/docs/build/html/_sources/input_description.rst.txt b/docs/build/html/_sources/input_description.rst.txt
index 27b97a81c51fc56ed48ebd970be05062490f1e2f..ee506fc23487557fe054015d11cc5e09c0ab0bf2 100644
--- a/docs/build/html/_sources/input_description.rst.txt
+++ b/docs/build/html/_sources/input_description.rst.txt
@@ -1,15 +1,13 @@
 Priors, input space and experimental design
 *******************************************
-.. note::
-	#TODO Write a short intro to uncertain inputs and sampling 
+The surrogate models, as used in BayesValidRox, consider model formulations where at least one of the input parameters is associated with uncertainty.
+This uncertainty can be described as probability distributions over possible values for the parameter.
 
-Input classes
-=============
 .. container:: twocol
 
    .. container:: leftside
    
-      Four classes contained in bayesvalidrox are associated with the inputs: :any:`bayesvalidrox.surrogate_models.inputs.Marginal`, :any:`bayesvalidrox.surrogate_models.inputs.Input`, :any:`bayesvalidrox.surrogate_models.input_space.InputSpace` and :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns`. 
+      Four classes contained in bayesvalidrox are associated with describing uncertain inputs: :any:`bayesvalidrox.surrogate_models.inputs.Marginal`, :any:`bayesvalidrox.surrogate_models.inputs.Input`, :any:`bayesvalidrox.surrogate_models.input_space.InputSpace` and :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns`. 
    
       Uncertain parameters are specified via their marginal distributions in :any:`bayesvalidrox.surrogate_models.inputs.Marginal` objects as either distribution types with associated parameters, or via a set of realizations.
       Supported distribution types include ``unif``, ``norm``, ``gamma``, ``beta``, ``lognorm``, ``expon`` and ``weibull``.
@@ -22,7 +20,7 @@ Input classes
    
       .. image:: ../diagrams/input_classes.png
          :width: 300
-         :alt: UML for input-related classes in bayesvalidrox
+         :alt: UML diagram for input-related classes in bayesvalidrox
    
 .. note::
    When using a polynomial-type surrogate setting ``rosenblatt`` to ``True`` results in all hermite polynomials.
@@ -58,15 +56,26 @@ If they are defined via distribution types, the ``name``, ``dist_type`` and ``pa
 
 If they are given via data, only ``name`` and ``input_data`` are relevant.
 
+>>> inputParams = np.random.uniform(-5,-5,100)
 >>> Inputs.add_marginals()
 >>> Inputs.Marginals[0].name = '$X$'
->>> Inputs.Marginals[0].input_data = inputParams[:, 0]
+>>> Inputs.Marginals[0].input_data = inputParams
 
 An experimental design can be constructed based on these inputs.
 
->>> ExpDesign = ExpDesign(Inputs)
+>>> ExpDesign = ExpDesigns(Inputs)
 
 Samples of the marginals can be created by specifying a sampling method and generating the wanted number of samples.
 
 >>> ExpDesign.sampling_method = 'latin_hypercube'
->>> samples = ExpDesign.generate_samples(100)
\ No newline at end of file
+>>> samples = ExpDesign.generate_samples(100)
+
+The generated samples can be visualized against their marginal distributions.
+
+>>> ExpDesign.plot_samples(samples)
+
+The results will be saved in the folder ``Outputs_Priors``.
+
+.. image:: ../../examples/user_guide/Outputs_Priors/prior_$X$.png
+   :width: 400
+   :alt: Generated samples against their marginal distribution
diff --git a/docs/build/html/_sources/packagedescription.rst.txt b/docs/build/html/_sources/packagedescription.rst.txt
index 33f94669090ebd964670c891f6af86d17e5eed4f..30164f68ce3cc9fab8eddc107552d004951a245c 100644
--- a/docs/build/html/_sources/packagedescription.rst.txt
+++ b/docs/build/html/_sources/packagedescription.rst.txt
@@ -1,38 +1,74 @@
 USER GUIDE
 **********
 
-Introductory theory
-===================
-.. note::
-   #TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.
+Installation
+------------
+BayesValidRox provides functionalities for describing uncertain parameters, building surrogate models based on model outputs and evaluating them with Bayesian validation methods.
+
+This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on.
+It can be installed with pip, best practice is to do so inside a virtual environment.
+
+.. code-block:: bash
+
+   python3 -m venv bayes_env
+   cd bayes_env
+   source bin/activate
+
+Here replace ``bayes_env`` with your preferred name.
+Then install the latest release of BayesValidRox inside the venv.
+
+.. code-block:: bash
+
+   pip install bayesvalidrox
 
+The current master can be installed by cloning the repository.
+
+.. code-block:: bash
+
+   git clone https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox.git
+   cd bayesvalidrox
+   pip install .
 
 Overview
 ========
-This package is split into multiple topics corresponding to the folder structure of the package.
+.. note::
+   #TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.
+
+This package is split into multiple aspects corresponding to its folder structure.
 
 
 .. image:: ../diagrams/folder_structure.png
    :width: 600
    :alt: Folder structure of **bayesvalidrox**
 
-The folder `surrogate_models` contains all the functions and classes that are necessary in order to create and train the surrogate model.
+The folder ``surrogate_models`` contains all the functions and classes that are necessary in order to create and train the surrogate model.
 This includes 
 
 * defining the input marginals
 * setting properties of the sampling in an experimental design
 * choosing the surrogate model and its properties
-* training the surrogate model on model evaluations in a straightforward or iterative manner
+* training the surrogate model on model evaluations in a straightforward manner or iteratively with active learning
+
+The computational model is linked via a ``pylink`` interface.
+
+Multiple post-processing options are available, including the calculation of Sobol' indices, checking the accuracy of the surrogate model and visualizations of the moments of the surrogate.
 
-The computational model is linked via a *pylink* interface.
-We split this into the aspects :any:`input_description` and :any:`surrogate_description` to provide insight into the options available in bayesvalidrox.
+Bayesian inference can be performed with rejection sampling or MCMC, while taking into account the estimated uncertainty of the data that the (surrogate) model is compared to.
+If multiple (surrogate) models are given, they can be compared against each other with pairwise Bayes Factors, model weights or a justifiability analysis.
 
-:any:`post_description` can be applied to trained surrogate models, or using the underlying models themselves.
+..
+   We split this into the aspects :any:`input_description` and :any:`surrogate_description` to provide insight into the options available in bayesvalidrox.
+..
+   :any:`post_description` can be applied to trained surrogate models, or using the underlying models themselves.
 
+The next pages lead through the topics given in BayesValidRox and describe the available classes and give brief examples for their use.
 
 .. toctree::
    :maxdepth: 1
    
    input_description
+   model_description
    surrogate_description
+   al_description
    post_description
+   bayes_description
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index ff72281e3b7ad1093b3d3492d5b4cf9bb231d093..2b0791a3b4af33fa58bd71d15659bc6600d809eb 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -162,8 +162,17 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <ul>
+<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="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>
-<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -173,12 +182,6 @@
 <li class="toctree-l2"><a class="reference internal" href="pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="packagedescription.html">USER GUIDE</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><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="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>
-</ul>
-</li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="api.html">API</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
@@ -975,6 +978,8 @@
         <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_moments">plot_moments() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
 </li>
         <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_post_params">plot_post_params() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+</li>
+        <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.plot_samples">plot_samples() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
 </li>
         <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_seq_design_diagnostics">plot_seq_design_diagnostics() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
 </li>
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index e3e61aa8fd8095aaf0004a83ea1cff6a53333516..73eb5a83969857400cd0116ccc7dfa0f48ee5b87 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.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="TUTORIAL" href="tutorial.html" />
+<link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="USER GUIDE" href="packagedescription.html" />
 
     <!-- Generated with Sphinx 7.3.7 and Furo 2023.09.10 -->
         <title>bayesvalidrox 1.0.0 documentation</title>
@@ -164,8 +164,17 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <ul>
+<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="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>
-<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -175,12 +184,6 @@
 <li class="toctree-l2"><a class="reference internal" href="pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="packagedescription.html">USER GUIDE</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><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="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>
-</ul>
-</li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="api.html">API</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
@@ -349,10 +352,10 @@ The functionality and options for the different classes is described more in-dep
 </section>
 <section id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
-<dl class="simple">
-<dt>This package runs under Python 3.9 for versions &lt;1.0.0 and 3.9+ from version 1.0.0 on, use pip to install:</dt><dd><p>$ pip install bayesvalidrox</p>
-</dd>
-</dl>
+<p>This package runs under Python 3.9 for versions &lt;1.0.0 and 3.9+ from version 1.0.0 on, use pip to install:</p>
+<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">
@@ -371,6 +374,10 @@ The functionality and options for the different classes is described more in-dep
 <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>
@@ -394,11 +401,6 @@ The functionality and options for the different classes is described more in-dep
 <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="packagedescription.html">USER GUIDE</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="packagedescription.html#introductory-theory">Introductory theory</a></li>
-<li class="toctree-l2"><a class="reference internal" href="packagedescription.html#overview">Overview</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>
@@ -421,12 +423,12 @@ The functionality and options for the different classes is described more in-dep
       <footer>
         
         <div class="related-pages">
-          <a class="next-page" href="tutorial.html">
+          <a class="next-page" href="packagedescription.html">
               <div class="page-info">
                 <div class="context">
                   <span>Next</span>
                 </div>
-                <div class="title">TUTORIAL</div>
+                <div class="title">USER GUIDE</div>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
@@ -484,6 +486,6 @@ The functionality and options for the different classes is described more in-dep
     <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/scripts/furo.js?v=32e29ea5"></script>
-    <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script async="async" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/build/html/input_description.html b/docs/build/html/input_description.html
index a652a5a36f34ce2603da08c3f2d6cd4544600d5f..39e16d2b9c9ebbb21b05daa969ec7defe54b2f01 100644
--- a/docs/build/html/input_description.html
+++ b/docs/build/html/input_description.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="Training surrogate models" href="surrogate_description.html" /><link rel="prev" title="PACKAGE DESCRIPTION" href="packagedescription.html" />
+<link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Models" href="model_description.html" /><link rel="prev" title="USER GUIDE" href="packagedescription.html" />
 
     <!-- Generated with Sphinx 7.3.7 and Furo 2023.09.10 -->
         <title>Priors, input space and experimental design - bayesvalidrox 1.0.0 documentation</title>
@@ -164,8 +164,17 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <ul class="current">
+<li class="toctree-l1 current has-children"><a class="reference internal" href="packagedescription.html">USER GUIDE</a><input checked="" 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 class="current">
+<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">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="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>
-<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -175,12 +184,6 @@
 <li class="toctree-l2"><a class="reference internal" href="pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 current has-children"><a class="reference internal" href="packagedescription.html">PACKAGE DESCRIPTION</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of PACKAGE DESCRIPTION</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
-<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Priors, input space and experimental design</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>
-</ul>
-</li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="api.html">API</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
@@ -334,22 +337,18 @@
         <article role="main">
           <section id="priors-input-space-and-experimental-design">
 <h1>Priors, input space and experimental design<a class="headerlink" href="#priors-input-space-and-experimental-design" title="Link to this heading">¶</a></h1>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>#TODO Write a short intro to uncertain inputs and sampling</p>
-</div>
-<section id="input-classes">
-<h2>Input classes<a class="headerlink" href="#input-classes" title="Link to this heading">¶</a></h2>
+<p>The surrogate models, as used in BayesValidRox, consider model formulations where at least one of the input parameters is associated with uncertainty.
+This uncertainty can be described as probability distributions over possible values for the parameter.</p>
 <div class="twocol docutils container">
 <div class="leftside docutils container">
-<p>Four classes contained in bayesvalidrox are associated with the inputs: <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html#bayesvalidrox.surrogate_models.inputs.Marginal" title="bayesvalidrox.surrogate_models.inputs.Marginal"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Marginal</span></code></a>, <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input" title="bayesvalidrox.surrogate_models.inputs.Input"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Input</span></code></a>, <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace" title="bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.input_space.InputSpace</span></code></a> and <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>.</p>
+<p>Four classes contained in bayesvalidrox are associated with describing uncertain inputs: <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html#bayesvalidrox.surrogate_models.inputs.Marginal" title="bayesvalidrox.surrogate_models.inputs.Marginal"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Marginal</span></code></a>, <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input" title="bayesvalidrox.surrogate_models.inputs.Input"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Input</span></code></a>, <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace" title="bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.input_space.InputSpace</span></code></a> and <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>.</p>
 <p>Uncertain parameters are specified via their marginal distributions in <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html#bayesvalidrox.surrogate_models.inputs.Marginal" title="bayesvalidrox.surrogate_models.inputs.Marginal"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Marginal</span></code></a> objects as either distribution types with associated parameters, or via a set of realizations.
 Supported distribution types include <code class="docutils literal notranslate"><span class="pre">unif</span></code>, <code class="docutils literal notranslate"><span class="pre">norm</span></code>, <code class="docutils literal notranslate"><span class="pre">gamma</span></code>, <code class="docutils literal notranslate"><span class="pre">beta</span></code>, <code class="docutils literal notranslate"><span class="pre">lognorm</span></code>, <code class="docutils literal notranslate"><span class="pre">expon</span></code> and <code class="docutils literal notranslate"><span class="pre">weibull</span></code>.</p>
 <p>All marginals contained in an <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input" title="bayesvalidrox.surrogate_models.inputs.Input"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Input</span></code></a> object should be defined in the same manner, mixing definitions via distribution type and sampels is not supported.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">rosenblatt</span></code> is set as <code class="docutils literal notranslate"><span class="pre">True</span></code>, then a Rosenblatt transform will be applied for training the surrogate.</p>
 </div>
 <div class="rightside docutils container">
-<a class="reference internal image-reference" href="_images/input_classes.png"><img alt="UML for input-related classes in bayesvalidrox" src="_images/input_classes.png" style="width: 300px;" /></a>
+<a class="reference internal image-reference" href="_images/input_classes.png"><img alt="UML diagram for input-related classes in bayesvalidrox" src="_images/input_classes.png" style="width: 300px;" /></a>
 </div>
 </div>
 <div class="admonition note">
@@ -364,7 +363,6 @@ This includes sampling from the distributions and applying the Rosenblatt transf
 The class <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> additionally contains methods and attributes related to sampling from the input space for static and iterative training of the surrogate model.
 Supported sampling methods include <code class="docutils literal notranslate"><span class="pre">random</span></code>, <code class="docutils literal notranslate"><span class="pre">latin-hypercube</span></code>, <code class="docutils literal notranslate"><span class="pre">sobol</span></code>, <code class="docutils literal notranslate"><span class="pre">halton</span></code>, <code class="docutils literal notranslate"><span class="pre">hammersley</span></code>, <code class="docutils literal notranslate"><span class="pre">chebyshev(FT)</span></code>, <code class="docutils literal notranslate"><span class="pre">grid(FT)</span></code> and <code class="docutils literal notranslate"><span class="pre">user</span></code> for user-defined sampling.</p>
 <p>The options for iterative metamodel training are detailed in <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a>.</p>
-</section>
 <section id="example">
 <h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
 <p>In practice, only the classes <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input" title="bayesvalidrox.surrogate_models.inputs.Input"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Input</span></code></a> and <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> are directly used.</p>
@@ -383,13 +381,14 @@ Supported sampling methods include <code class="docutils literal notranslate"><s
 </pre></div>
 </div>
 <p>If they are given via data, only <code class="docutils literal notranslate"><span class="pre">name</span></code> and <code class="docutils literal notranslate"><span class="pre">input_data</span></code> are relevant.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">Inputs</span><span class="o">.</span><span class="n">add_marginals</span><span class="p">()</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">inputParams</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Inputs</span><span class="o">.</span><span class="n">add_marginals</span><span class="p">()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Inputs</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;$X$&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">Inputs</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">input_data</span> <span class="o">=</span> <span class="n">inputParams</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Inputs</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">input_data</span> <span class="o">=</span> <span class="n">inputParams</span>
 </pre></div>
 </div>
 <p>An experimental design can be constructed based on these inputs.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span> <span class="o">=</span> <span class="n">ExpDesign</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span> <span class="o">=</span> <span class="n">ExpDesigns</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Samples of the marginals can be created by specifying a sampling method and generating the wanted number of samples.</p>
@@ -397,6 +396,12 @@ Supported sampling methods include <code class="docutils literal notranslate"><s
 <span class="gp">&gt;&gt;&gt; </span><span class="n">samples</span> <span class="o">=</span> <span class="n">ExpDesign</span><span class="o">.</span><span class="n">generate_samples</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
 </pre></div>
 </div>
+<p>The generated samples can be visualized against their marginal distributions.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">plot_samples</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The results will be saved in the folder <code class="docutils literal notranslate"><span class="pre">Outputs_Priors</span></code>.</p>
+<a class="reference internal image-reference" href="_images/prior_$X$.png"><img alt="Generated samples against their marginal distribution" src="_images/prior_%24X%24.png" style="width: 400px;" /></a>
 </section>
 </section>
 
@@ -405,12 +410,12 @@ Supported sampling methods include <code class="docutils literal notranslate"><s
       <footer>
         
         <div class="related-pages">
-          <a class="next-page" href="surrogate_description.html">
+          <a class="next-page" href="model_description.html">
               <div class="page-info">
                 <div class="context">
                   <span>Next</span>
                 </div>
-                <div class="title">Training surrogate models</div>
+                <div class="title">Models</div>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
@@ -421,7 +426,7 @@ Supported sampling methods include <code class="docutils literal notranslate"><s
                   <span>Previous</span>
                 </div>
                 
-                <div class="title">PACKAGE DESCRIPTION</div>
+                <div class="title">USER GUIDE</div>
                 
               </div>
             </a>
@@ -456,7 +461,6 @@ Supported sampling methods include <code class="docutils literal notranslate"><s
           <div class="toc-tree">
             <ul>
 <li><a class="reference internal" href="#">Priors, input space and experimental design</a><ul>
-<li><a class="reference internal" href="#input-classes">Input classes</a></li>
 <li><a class="reference internal" href="#example">Example</a></li>
 </ul>
 </li>
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index ca48356e512ebc419a6d9db47d51bb94d10958ac..b24151365f5b50ce51d7b5ed10cfdff340f22cf1 100644
Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ
diff --git a/docs/build/html/packagedescription.html b/docs/build/html/packagedescription.html
index 448b5e1e5d7cd4f8e890b5f460d92e97b074cc6f..66819e77f7eef8c7c7924b8131ea89426cffc523 100644
--- a/docs/build/html/packagedescription.html
+++ b/docs/build/html/packagedescription.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="Priors, input space and experimental design" href="input_description.html" /><link rel="prev" title="Example: pollution" href="pollution.html" />
+<link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Priors, input space and experimental design" href="input_description.html" /><link rel="prev" title="Surrogate-assisted Bayesian validation of computational models" href="index.html" />
 
     <!-- Generated with Sphinx 7.3.7 and Furo 2023.09.10 -->
         <title>USER GUIDE - bayesvalidrox 1.0.0 documentation</title>
@@ -164,8 +164,17 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <ul class="current">
+<li class="toctree-l1 current has-children current-page"><a class="current reference internal" href="#">USER GUIDE</a><input checked="" 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="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>
-<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -175,12 +184,6 @@
 <li class="toctree-l2"><a class="reference internal" href="pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 current has-children current-page"><a class="current reference internal" href="#">USER GUIDE</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><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="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>
-</ul>
-</li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="api.html">API</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
@@ -334,36 +337,60 @@
         <article role="main">
           <section id="user-guide">
 <h1>USER GUIDE<a class="headerlink" href="#user-guide" title="Link to this heading">¶</a></h1>
-<section id="introductory-theory">
-<h2>Introductory theory<a class="headerlink" href="#introductory-theory" title="Link to this heading">¶</a></h2>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
+<p>BayesValidRox provides functionalities for describing uncertain parameters, building surrogate models based on model outputs and evaluating them with Bayesian validation methods.</p>
+<p>This package runs under Python 3.9 for versions &lt;1.0.0 and 3.9+ from version 1.0.0 on.
+It can be installed with pip, best practice is to do so inside a virtual environment.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>-m<span class="w"> </span>venv<span class="w"> </span>bayes_env
+<span class="nb">cd</span><span class="w"> </span>bayes_env
+<span class="nb">source</span><span class="w"> </span>bin/activate
+</pre></div>
+</div>
+<p>Here replace <code class="docutils literal notranslate"><span class="pre">bayes_env</span></code> with your preferred name.
+Then install the latest release of BayesValidRox inside the venv.</p>
+<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>The current master can be installed by cloning the repository.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox.git
+<span class="nb">cd</span><span class="w"> </span>bayesvalidrox
+pip<span class="w"> </span>install<span class="w"> </span>.
+</pre></div>
+</div>
+<section id="overview">
+<h3>Overview<a class="headerlink" href="#overview" title="Link to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>#TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.</p>
 </div>
-</section>
-<section id="overview">
-<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">¶</a></h2>
-<p>This package is split into multiple topics corresponding to the folder structure of the package.</p>
+<p>This package is split into multiple aspects corresponding to its folder structure.</p>
 <a class="reference internal image-reference" href="_images/folder_structure.png"><img alt="Folder structure of **bayesvalidrox**" src="_images/folder_structure.png" style="width: 600px;" /></a>
-<p>The folder <cite>surrogate_models</cite> contains all the functions and classes that are necessary in order to create and train the surrogate model.
+<p>The folder <code class="docutils literal notranslate"><span class="pre">surrogate_models</span></code> contains all the functions and classes that are necessary in order to create and train the surrogate model.
 This includes</p>
 <ul class="simple">
 <li><p>defining the input marginals</p></li>
 <li><p>setting properties of the sampling in an experimental design</p></li>
 <li><p>choosing the surrogate model and its properties</p></li>
-<li><p>training the surrogate model on model evaluations in a straightforward or iterative manner</p></li>
+<li><p>training the surrogate model on model evaluations in a straightforward manner or iteratively with active learning</p></li>
 </ul>
-<p>The computational model is linked via a <em>pylink</em> interface.
-We split this into the aspects <a class="reference internal" href="input_description.html"><span class="doc">Priors, input space and experimental design</span></a> and <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a> to provide insight into the options available in bayesvalidrox.</p>
-<p><a class="reference internal" href="post_description.html"><span class="doc">Postprocessing, Bayesian inference and Bayesian model comparison</span></a> can be applied to trained surrogate models, or using the underlying models themselves.</p>
+<p>The computational model is linked via a <code class="docutils literal notranslate"><span class="pre">pylink</span></code> interface.</p>
+<p>Multiple post-processing options are available, including the calculation of Sobol’ indices, checking the accuracy of the surrogate model and visualizations of the moments of the surrogate.</p>
+<p>Bayesian inference can be performed with rejection sampling or MCMC, while taking into account the estimated uncertainty of the data that the (surrogate) model is compared to.
+If multiple (surrogate) models are given, they can be compared against each other with pairwise Bayes Factors, model weights or a justifiability analysis.</p>
+<p>The next pages lead through the topics given in BayesValidRox and describe the available classes and give brief examples for their use.</p>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="input_description.html">Priors, input space and experimental design</a></li>
+<li class="toctree-l1"><a class="reference internal" href="model_description.html">Models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l1"><a class="reference internal" href="post_description.html">Postprocessing, Bayesian inference and Bayesian model comparison</a></li>
+<li class="toctree-l1"><a class="reference internal" href="al_description.html">Active learning: iteratively expanding the training set</a></li>
+<li class="toctree-l1"><a class="reference internal" href="post_description.html">Postprocessing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bayes_description.html">Bayesian inference and multi-model comparison</a></li>
 </ul>
 </div>
 </section>
+</section>
 </section>
 
         </article>
@@ -380,14 +407,14 @@ We split this into the aspects <a class="reference internal" href="input_descrip
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
-          <a class="prev-page" href="pollution.html">
+          <a class="prev-page" href="index.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">Example: pollution</div>
+                <div class="title">Home</div>
                 
               </div>
             </a>
@@ -422,10 +449,12 @@ We split this into the aspects <a class="reference internal" href="input_descrip
           <div class="toc-tree">
             <ul>
 <li><a class="reference internal" href="#">USER GUIDE</a><ul>
-<li><a class="reference internal" href="#introductory-theory">Introductory theory</a></li>
+<li><a class="reference internal" href="#installation">Installation</a><ul>
 <li><a class="reference internal" href="#overview">Overview</a></li>
 </ul>
 </li>
+</ul>
+</li>
 </ul>
 
           </div>
@@ -439,5 +468,6 @@ We split this into the aspects <a class="reference internal" href="input_descrip
     <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/scripts/furo.js?v=32e29ea5"></script>
+    <script async="async" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
     </body>
 </html>
\ No newline at end of file
diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html
index 3954a396b74fba3c224d98fddc6786a7ef086ef8..a10614c9b79526a3d472e0d16dc897c071d4208e 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@@ -162,8 +162,17 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <ul>
+<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="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>
-<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -173,12 +182,6 @@
 <li class="toctree-l2"><a class="reference internal" href="pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="packagedescription.html">USER GUIDE</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><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="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>
-</ul>
-</li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="api.html">API</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
diff --git a/docs/build/html/search.html b/docs/build/html/search.html
index 16e0b0a1bdc3aadacce5c2e10f53a761c3883aa1..8fe37498ef5854389bf8baad867f2b6e492744ad 100644
--- a/docs/build/html/search.html
+++ b/docs/build/html/search.html
@@ -161,8 +161,17 @@
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
   <ul>
+<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="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>
-<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</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 EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="examples.html">EXAMPLES</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of EXAMPLES</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><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>
@@ -172,12 +181,6 @@
 <li class="toctree-l2"><a class="reference internal" href="pollution.html">Pollution</a></li>
 </ul>
 </li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="packagedescription.html">USER GUIDE</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><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="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>
-</ul>
-</li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="api.html">API</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l2 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
 <li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index f10e953545b75012a0af026e9b0228e68df4e8c9..76ddf25291fa5a07e2ce8ce53dfbf77a77333270 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
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"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, "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/diagrams/active_learning.dot b/docs/diagrams/active_learning.dot
new file mode 100644
index 0000000000000000000000000000000000000000..31920d725f3f17cf5f3235a0feb74e3c8f276d7b
--- /dev/null
+++ b/docs/diagrams/active_learning.dot
@@ -0,0 +1,21 @@
+digraph pyUML {
+Input [label="{Input||}", shape=record];
+InputSpace [label="{InputSpace||}", shape=record];
+ExpDesigns [label="{ExpDesigns|+ sampling_method : string\l+ hdf5_file : string\l+ n_new_samples : int\l+ n_max_samples : int\l+ mod_LOO_threshold : double\l+ tradeoff_scheme : string\l+ n_canddidate : int\l+ explore_method : string\l+ exploit_method : string\l+ util_func : string\l+ n_cand_groups : int\l+ n_replication : int\l+ post_snapshot : bool\l+ step_snapshot : int\l+ max_a_post : list\l+ adapt_verbose : bool\l+ max_func_itr : int\l+ out_dir : string\l|+ generate_samples()\l+ generate_ED()\l+ read_from_file()\l+ random_sampler()\l+ pcm_sampler()\l+ plot_samples()\l}", shape=record];
+ExpDesigns -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+PyLinkForwardModel [label="{PyLinkForwardModel|+ link_type : string\l+ name : string\l+ py_file : string\l+ func_args : dict\l+ shell_command : string\l+ input_file : string\l+ input_template : string\l+ aux_file : string\l+ exe_path : string\l+ output_file_names : list\l+ output_names : list\l+ output_parser : string\l+ multi_process : bool\l+ n_cpus : int\l+ meas_file : string\l+ meas_file_valid : string\l+ mc_ref_file : string\l+ obs_dict : dict\l+ obs_dict_valid : dict\l+ mc_ref_dict : dict\l|+ read_observation()\l+ read_output()\l+ update_input_params()\l+ run_command()\l+ run_forwardmodel()\l+ run_model_parallel()\l+ uMBridge_model()\l+ _store_simulations()\l+ zip_subdirs()\l}", shape=record];
+MetaModel [label="{MetaModel|+ input_obj : Input\l+ meta_model_type : string\l+ pce_reg_method : string\l+ bootstrap_method : string\l+ n_bootstrap_itrs : int\l+ pce_deg : int\l+ pce_q_norm : double\l+ dim_red_method : string\l+ apply_constraints : bool\l+ verbose : bool\l|+ build_metamodel()\l+ fit()\l+ update_pce_coeffs()\l+ add_InputSpace()\l+ univ_basis_vals()\l+ regression()\l+ adaptive_regression()\l+ pca_transformation()\l+ eval_metamodel()\l+ copy_meta_model_opts()\l+ _select_degree()\l+ generate_polynomials()\l+ _compute_pce_moments()\l}", shape=record];
+MetaModel -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+MetaModel -> InputSpace  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine [label="{Engine|+ MetaModel : MetaModel\l+ Model : Model\l+ ExpDesign : ExpDesigns\l+ parallel : bool\l+ trained : bool\l|+ start_engine()\l+ train_normal()\l+ train_sequential()\l+ eval_metamodel()\l+ train_seq_design()\l+ util_VarBasedDesign()\l+ util_BayesianActiveDesign()\l+ util_BayesianDesign()\l+ run_util_func()\l+ dual_annealing()\l+ tradeoff_weights()\l+ choose_next_sample()\l+ util_AlphOptDesign()\l+ _normpdt()\l+ _corr_factor_BME()\l+ _posteriorPlot()\l+ _BME_Calculator()\l+ _validError()\l+ _error_Mean_Std()\l}", shape=record];
+Engine -> PyLinkForwardModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> MetaModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Exploration [label="{Exploration|+ ExpDesign : ExpDesigns\l+ n_candidate : int\l+ mc_criterion : string\l|+ get_exploration_samples()\l+ get_vornoi_samples()\l+ get_mc_samples()\l+ approximate_voronoi()\l+ _build_dist_matrix_point()\l}", shape=record];
+Exploration -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> Exploration  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Discrepancy [label="{Discrepancy|+ InputDosc : Input\l+ disc_type : string\l+ parameters : list\l|+ get_sample()\l}", shape=record];
+Discrepancy -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Discrepancy -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> Discrepancy  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+}
diff --git a/docs/diagrams/active_learning_reduced.dot b/docs/diagrams/active_learning_reduced.dot
new file mode 100644
index 0000000000000000000000000000000000000000..948804b9b766600be60235b01ef5531eeac548e9
--- /dev/null
+++ b/docs/diagrams/active_learning_reduced.dot
@@ -0,0 +1,17 @@
+digraph pyUML {
+Input [label="{Input||}", shape=record];
+ExpDesigns [label="{ExpDesigns|+ sampling_method : string\l+ hdf5_file : string\l+ n_new_samples : int\l+ n_max_samples : int\l+ mod_LOO_threshold : double\l+ tradeoff_scheme : string\l+ n_canddidate : int\l+ explore_method : string\l+ exploit_method : string\l+ util_func : string\l+ n_cand_groups : int\l+ n_replication : int\l+ post_snapshot : bool\l+ step_snapshot : int\l+ max_a_post : list\l+ adapt_verbose : bool\l+ max_func_itr : int\l+ out_dir : string\l|+ generate_samples()\l+ generate_ED()\l+ read_from_file()\l+ random_sampler()\l+ pcm_sampler()\l+ plot_samples()\l}", shape=record];
+PyLinkForwardModel [label="{PyLinkForwardModel||}", shape=record];
+MetaModel [label="{MetaModel||}", shape=record];
+Engine [label="{Engine|+ MetaModel : MetaModel\l+ Model : Model\l+ ExpDesign : ExpDesigns\l+ parallel : bool\l+ trained : bool\l|+ start_engine()\l+ train_normal()\l+ train_sequential()\l+ eval_metamodel()\l+ train_seq_design()\l+ util_VarBasedDesign()\l+ util_BayesianActiveDesign()\l+ util_BayesianDesign()\l+ run_util_func()\l+ dual_annealing()\l+ tradeoff_weights()\l+ choose_next_sample()\l+ util_AlphOptDesign()\l+ _normpdt()\l+ _corr_factor_BME()\l+ _posteriorPlot()\l+ _BME_Calculator()\l+ _validError()\l+ _error_Mean_Std()\l}", shape=record];
+Engine -> PyLinkForwardModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> MetaModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Exploration [label="{Exploration|+ ExpDesign : ExpDesigns\l+ n_candidate : int\l+ mc_criterion : string\l|+ get_exploration_samples()\l+ get_vornoi_samples()\l+ get_mc_samples()\l+ approximate_voronoi()\l+ _build_dist_matrix_point()\l}", shape=record];
+Exploration -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> Exploration  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Discrepancy [label="{Discrepancy|+ InputDisc : Input\l+ disc_type : string\l+ parameters : list\l|+ get_sample()\l}", shape=record];
+Discrepancy -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Discrepancy -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> Discrepancy  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+}
diff --git a/docs/diagrams/bayesian_validation.dot b/docs/diagrams/bayesian_validation.dot
new file mode 100644
index 0000000000000000000000000000000000000000..46814289b17c73adefd12d435b34168a20c8dd24
--- /dev/null
+++ b/docs/diagrams/bayesian_validation.dot
@@ -0,0 +1,13 @@
+digraph pyUML {
+ExpDesigns [label="{ExpDesigns||}", shape=record];
+PyLinkForwardModel [label="{PyLinkForwardModel||}", shape=record];
+MetaModel [label="{MetaModel||}", shape=record];
+Engine [label="{Engine||}", shape=record];
+Engine -> PyLinkForwardModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> MetaModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Discrepancy [label="{Discrepancy||}", shape=record];
+BayesInference [label="{BayesInference|+ engine : Engine\l+ discrepancy : Discrepancy\l+ emulator : bool\l+ name : string\l+ bootatrap : bool\l+ req_outputs : list\l+ selected_indices : list\l+ prior_samples : array\l+ n_prior_samples : int\l+ measured_data : dict\l+ inference_method : string\l+ mcmc_params : dict\l+ bayes_loocv : bool\l+ n_bootstrap_itrs : int\l+ perturbed_data : lsit\l+ bootstrap_noise : double\l+ just_analysis : bool\l+ valid_metrics : list\l+ plot_post_pred : bool\l+ plot_map_pred : bool\l+ max_a_posteriori : string\l+ corner_title_fmt : string\l+ out_dir : string\l+ bmc : bool\l|+ setup_inference()\l+ create_inference()\l+ create_error_model()\l+ perform_bootstrap()\l+ _perturb_data()\l+ _eval_model()\l+ normpdf()\l+ _coor_Factor_BME()\l+ _rejection_sampling()\l+ _posterior_predictive()\l+ _plot_max_a_posteriori()\l+ plot_post_params()\l+ plot_log_BME()\l+ _plot_post_predictive()\l}", shape=record];
+BayesInference -> Engine  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+BayesInference -> Discrepancy  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+}
diff --git a/docs/diagrams/class_diagram.py b/docs/diagrams/class_diagram.py
index 78b7900641ab74b8f6abe33533f87f3d93669b3d..71567b36193c312d2f627a4f3a3ca04e04bfb350 100644
--- a/docs/diagrams/class_diagram.py
+++ b/docs/diagrams/class_diagram.py
@@ -6,155 +6,881 @@ Interaction of classes in `bayesvalidrox`
 import graphviz
 from pyUML import Graph, UMLClass
 
-# List all classes and functions outside classes
-classes = {'PyLinkForwardModel':{'type_':'Model'}, 
-           'BayesInference':{'type_':'Bayes'},  
-           'BayesModelComparison':{'type_':'Bayes'}, 
-           'Discrepancy':{'type_':'Bayes'}, 
-           'MCMC':{'type_':'Bayes'}, 
-           'PostProcessing':{'type_':'PostProcessing'}, 
-           'BayesLinearRegression':{'type_':'training'}, 
-           'EBLinearRegression':{'type_':'training'}, 
-           'VBLinearRegression':{'type_':'training'}, 
-           'Engine':{'type_':'Engine'}, 
-           'ExpDesigns':{'type_':'Design'}, 
-           'Exploration':{'type_':'Engine'}, 
-           'InputSpace':{'type_':'Design'}, 
-           'Input':{'type_':'Design'}, 
-           'Marginal':{'type_':'Design'}, 
-           'OrthogonalMatchingPursuit':{'type_':'training'}, 
-           'RegressionFastARD':{'type_':'training'}, 
-           'RegressionFastLaplace':{'type_':'training'}, 
-           'MetaModel':{'type_':'MetaModel'}}
-
-free_functions = ['gelman_rubin', 'within_range', 'adaptPlot','apoly_construction',
-                  'gamma_mean', 'hellinger_distance', 'logpdf', 'subdomain', 'eval_rec_rule',
-                  'eval_rec_rule_arbitrary', 'eval_univ_basis', 'poly_rec_coeffs', 'check_ranges',
-                  'cross_truncate', 'glexindex', 'corr', 'update_precisions',
-                  'corr_loocv_error', 'create_psi', 'gaussian_process_emulator']
-
-if 0:
-    # Create digraph object
-    g = graphviz.Digraph('classinteraction', filename='classinteraction.gv', engine='fdp')
-    
-    # Add the classes and functions as nodes
-    g.attr('node', shape='box')
-    #for c in classes:
-    #    g.node(c)
-        
-    #g.attr('node', shape='ellipse')
-    #for f in free_functions:
-    #    g.node(f)
-        
-    # Create topic-based subclusters
-    with g.subgraph(name='cluster_input') as b:
-        b.edge('Marginal', 'Input')
-    g.edge('cluster_input', 'ExpDesigns')
-    g.edge('cluster_input', 'MetaModel')
-    
-    g.edge('InputSpace', 'MetaModel')
-    
-    
-    
-    with g.subgraph(name='cluster_regression') as a:
-        for i in classes:
-            if classes[i]['type_'] == 'training':
-                a.node(i)
-    g.edge('cluster_regression', 'MetaModel')
-        
-    
-    g.edge('PyLinkForwardModel', 'Engine')
-    g.edge('ExpDesigns', 'Engine')
-    g.edge('MetaModel', 'Engine')
-    g.edge('Exploration', 'Engine')
-    
-    with g.subgraph(name='cluster_post') as c:
-        c.node('PostProcessing')
-        c.edge('BayesInference', 'BayesModelComparison')
-        c.edge('MCMC', 'BayesInference')
-        
-    g.edge('Engine', 'cluster_post')
-    g.edge('Discrepancy', 'Engine')
-    g.edge('Discrepancy', 'BayesInference')
-    # Add the class-class interactions
-    if 0:
-        g.edge('InputSpace', 'MetaModel')
-        g.edge('Engine', 'PostProcessing')
-        g.edge('Engine', 'BayesInference')
-        g.edge('Engine', 'BayesModelComparison')
-    
-    g.view()
-
 def print_and_save(graph, name):
-    print(graph.to_string())
+    #print(graph.to_string())
     graph.write_raw(f"{name}.dot")
     graph.write(f"{name}.png", "dot", "png")
 
-# Create UML for the input classes
-graph = Graph('pyUML')
-
-# Add input class
-inputs = UMLClass('Input',
-                  attributes={
-                          'Marginals':'list',
-                          'Rosenblatt':'bool'
-                      },
-                  methods=['add_marginals()']
-                  )
-graph.add_class(inputs)
-
-# Add marginal class
-marginals = UMLClass('Marginal',
-                     attributes={
-                             'name':'string',
-                             'dist_type': 'string',
-                             'parameters': 'list',
-                             'input_data':'array',
-                             'moments': 'list'
-                         }
+def generate_input_uml():
+    # Create UML for the input classes
+    graph = Graph('pyUML')
+    
+    # Add input class
+    inputs = UMLClass('Input',
+                      attributes={
+                              'Marginals':'list',
+                              'Rosenblatt':'bool'
+                          },
+                      methods=['add_marginals()']
+                      )
+    graph.add_class(inputs)
+    
+    # Add marginal class
+    marginals = UMLClass('Marginal',
+                         attributes={
+                                 'name':'string',
+                                 'dist_type': 'string',
+                                 'parameters': 'list',
+                                 'input_data':'array',
+                                 'moments': 'list'
+                             }
+                         )
+    graph.add_class(marginals)
+    graph.add_composition(marginals, inputs, multiplicity_parent=1, multiplicity_child='1..*')
+    
+    inputspace = UMLClass('InputSpace',
+                          attributes = {
+                              'input_obj':'Input',
+                              'meta_Model_type':'string'
+                              },
+                          methods = [
+                              'check_valid_inputs()', 'init_param_space()', 
+                              'build_polytypes()', 'transform()'
+                              ]
+                          )
+    graph.add_class(inputspace)
+    graph.add_composition(inputs, inputspace, multiplicity_parent=1, multiplicity_child=1)
+    
+    expdesign = UMLClass('ExpDesigns',
+                         attributes = {
+                             'sampling_method': 'string',
+                             'hdf5_file': 'string',
+                             'n_new_samples': 'int',
+                             'n_max_samples': 'int',
+                             'mod_LOO_threshold': 'double',
+                             'tradeoff_scheme':'string',
+                             'n_canddidate': 'int',
+                             'explore_method':'string',
+                             'exploit_method': 'string',
+                             'util_func':'string',
+                             'n_cand_groups': 'int',
+                             'n_replication': 'int',
+                             'post_snapshot': 'bool', 
+                             'step_snapshot':'int',
+                             'max_a_post': 'list',
+                             'adapt_verbose':'bool',
+                             'max_func_itr': 'int',
+                             'out_dir': 'string'
+                             },
+                         methods = [
+                             'generate_samples()', 'generate_ED()', 
+                             'read_from_file()', 'random_sampler()',
+                             'pcm_sampler()', 'plot_samples()'
+                             ])
+    graph.add_class(expdesign)
+    graph.add_implementation(expdesign, inputspace)
+    
+    print_and_save(graph, 'input_classes')
+    
+def generate_input_uml_reduced():
+    # Create UML for the input classes
+    graph = Graph('pyUML')
+    
+    # Add input class
+    inputs = UMLClass('Input',
+                      attributes={
+                              'Marginals':'list',
+                              'Rosenblatt':'bool'
+                          },
+                      methods=['add_marginals()']
+                      )
+    graph.add_class(inputs)
+    
+    # Add marginal class
+    marginals = UMLClass('Marginal',
+                         attributes={
+                                 'name':'string',
+                                 'dist_type': 'string',
+                                 'parameters': 'list',
+                                 'input_data':'array',
+                                 'moments': 'list'
+                             }
+                         )
+    graph.add_class(marginals)
+    graph.add_composition(marginals, inputs, multiplicity_parent=1, multiplicity_child='1..*')
+    
+    inputspace = UMLClass('InputSpace',
+                          attributes = {
+                              'input_obj':'Input',
+                              'meta_Model_type':'string'
+                              },
+                          methods = [
+                              'check_valid_inputs()', 'init_param_space()', 
+                              'transform()'
+                              ]
+                          )
+    graph.add_class(inputspace)
+    graph.add_composition(inputs, inputspace, multiplicity_parent=1, multiplicity_child=1)
+    
+    expdesign = UMLClass('ExpDesigns',
+                         attributes = {
+                             'sampling_method': 'string',
+                             'hdf5_file': 'string',
+                             'out_dir': 'string'
+                             },
+                         methods = [
+                             'generate_samples()', 'generate_ED()', 
+                             'read_from_file()', 'random_sampler()',
+                             'pcm_sampler()', 'plot_samples()'
+                             ])
+    graph.add_class(expdesign)
+    graph.add_implementation(expdesign, inputspace)
+    
+    print_and_save(graph, 'input_classes_reduced')
+    
+def generate_training_uml():
+    """
+    Generates the uml for static training and related classes
+    """
+    graph = Graph('pyUML')
+    
+    # Add input class
+    inputs = UMLClass('Input')
+    graph.add_class(inputs)
+    inputspace = UMLClass('InputSpace')
+    graph.add_class(inputspace)
+    
+    expdesign = UMLClass('ExpDesigns',
+                         attributes = {
+                             'sampling_method': 'string',
+                             'hdf5_file': 'string',
+                             'n_new_samples': 'int',
+                             'n_max_samples': 'int',
+                             'mod_LOO_threshold': 'double',
+                             'tradeoff_scheme':'string',
+                             'n_canddidate': 'int',
+                             'explore_method':'string',
+                             'exploit_method': 'string',
+                             'util_func':'string',
+                             'n_cand_groups': 'int',
+                             'n_replication': 'int',
+                             'post_snapshot': 'bool', 
+                             'step_snapshot':'int',
+                             'max_a_post': 'list',
+                             'adapt_verbose':'bool',
+                             'max_func_itr': 'int',
+                             'out_dir': 'string'
+                             },
+                         methods = [
+                             'generate_samples()', 'generate_ED()', 
+                             'read_from_file()', 'random_sampler()',
+                             'pcm_sampler()', 'plot_samples()'
+                             ])
+    graph.add_class(expdesign)
+    graph.add_composition(inputs, expdesign, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    model = UMLClass('PyLinkForwardModel',
+                     attributes = {
+                         'link_type':'string', 
+                         'name':'string', 
+                         'py_file':'string',
+                        'func_args':'dict', 
+                        'shell_command':'string', 
+                        'input_file':'string',
+                        'input_template':'string', 
+                        'aux_file':'string', 
+                        'exe_path':'string',
+                        'output_file_names':'list', 
+                        'output_names':'list', 
+                        'output_parser':'string',
+                        'multi_process':'bool', 
+                        'n_cpus':'int', 
+                        'meas_file':'string',
+                        'meas_file_valid':'string', 
+                        'mc_ref_file':'string', 
+                        'obs_dict':'dict',
+                        'obs_dict_valid':'dict', 
+                        'mc_ref_dict':'dict'
+                         },
+                     methods = [
+                         'read_observation()', 'read_output()','update_input_params()',
+                         'run_command()', 'run_forwardmodel()', 'run_model_parallel()',
+                         'uMBridge_model()', '_store_simulations()', 'zip_subdirs()'
+                         ]
                      )
-graph.add_class(marginals)
-graph.add_composition(marginals, inputs, multiplicity_parent=1, multiplicity_child='1..*')
-
-inputspace = UMLClass('InputSpace',
+    graph.add_class(model)
+    
+    metamod = UMLClass('MetaModel',
+                       attributes = {
+                           'input_obj':'Input',
+                           'meta_model_type': 'string',
+                           'pce_reg_method':'string',
+                           'bootstrap_method':'string',
+                           'n_bootstrap_itrs':'int',
+                           'pce_deg':'int',
+                           'pce_q_norm':'double',
+                           'dim_red_method':'string',
+                           'apply_constraints':'bool',
+                           'verbose':'bool'
+                           },
+                       methods = [
+                           'build_metamodel()', 'fit()', 'update_pce_coeffs()',
+                           'add_InputSpace()', 'univ_basis_vals()', 'regression()',
+                           'adaptive_regression()', 'pca_transformation()', 'eval_metamodel()',
+                           'create_model_error()', 'eval_model_error()', 'copy_meta_model_opts()', 
+                           '_select_degree()', 'generate_polynomials()', '_compute_pce_moments()'
+                           ]
+                       )
+    graph.add_class(metamod)
+    graph.add_composition(inputs, metamod, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(inputspace, metamod, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    engine = UMLClass('Engine',
                       attributes = {
-                          'input_obj':'Input',
-                          'meta_Model_type':'string'
+                          'MetaModel':'MetaModel',
+                          'Model':'Model',
+                          'ExpDesign':'ExpDesigns',
+                          'parallel': 'bool', 
+                          'trained':'bool',
                           },
                       methods = [
-                          'check_valid_inputs()', 'init_param_space()', 
-                          'build_polytypes()', 'transform()'
+                          'start_engine()', 'train_normal()', 'train_sequential()', 
+                          'eval_metamodel()', 'train_seq_design()', 'util_VarBasedDesign()', 
+                          'util_BayesianActiveDesign()', 'util_BayesianDesign()', 'run_util_func()', 
+                          'dual_annealing()', 'tradeoff_weights()', 'choose_next_sample()', 
+                          'util_AlphOptDesign()', '_normpdt()', '_corr_factor_BME()', 
+                          '_posteriorPlot()', '_BME_Calculator()', '_validError()', '_error_Mean_Std()'
                           ]
                       )
-graph.add_class(inputspace)
-graph.add_composition(inputs, inputspace, multiplicity_parent=1, multiplicity_child=1)
-
-expdesign = UMLClass('ExpDesigns',
+    graph.add_class(engine)
+    graph.add_composition(model, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(expdesign, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(metamod, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    
+    # Add the regression Classes
+    Breg = UMLClass('BayesianLinearRegression',
+                      attributes = {
+                          'n_iter':'int',
+                          'tol': 'double',
+                          'fit_intercept':'bool',
+                          'copy_X':'bool',
+                          'verbose':'bool'
+                          },
+                      methods = [
+                          '_check_convergence()', '_center_data()', 'predict_dist()'
+                          ]
+                      )
+    graph.add_class(Breg)
+    vBreg = UMLClass('VBLinearRegression',
+                      attributes = {
+                          'optimizer':'string',
+                          'alpha':'double',
+                          'normalize':'bool',
+                          'scores': 'list',
+                          'perfect_fit_tol': 'double',
+                          },
+                      methods = [
+                          'fit()', 'predict()'
+                          ]
+                      )
+    graph.add_class(vBreg)
+    eBreg = UMLClass('EBLinearRegression',
+                      attributes = {
+                          'a':'double',
+                        'b':'double',
+                        'c':'double',
+                        'd':'double'
+                          },
+                      methods = [
+                          'fit()', 'predict()', 'postrior_weights()'
+                          ]
+                      )
+    graph.add_class(eBreg)
+    omp = UMLClass('OrthogonalMatchingPursuit',
+                      attributes = {
+                      'fit_intercept':'bool',
+                      'normalize':'bool',
+                      'copy_X':'bool',
+                      'verbose':'bool'
+                          },
+                      methods = [
+                          '_preprocess_data()', 'fit()', 'predict()', 'loo_error()',
+                          'blockwise_inverse()'
+                          ]
+                      )
+    graph.add_class(omp)
+    regArd = UMLClass('RegressionFastARD',
+                      attributes = {
+                          'n_iter':'int',
+                          'start':'list',
+                          'tol':'double',
+                          'fit_intercept':'bool',
+                          'normalize':'bool',
+                          'copy_X':'bool',
+                          'compute_score':'bool',
+                          'verbose':'bool'
+                          },
+                      methods = [
+                          '_preprocess_data()', 'fit()', 'log_marginal_like()',
+                          'predict()', '_posterior_dist()', '_sparsity_quality()'
+                          ]
+                      )
+    graph.add_class(regArd)
+    regLap = UMLClass('RegressionFastLaplace',
+                      attributes = {
+                          'n_iter':'int',
+                          'n_Kfold':'int',
+                          'tol':'double',
+                          'fit_intercept':'bool',
+                          'bias_term':'bool',
+                          'copy_X':'bool',
+                          'verbose':'bool'
+                          },
+                      methods = [
+                          '_center_data()', 'fit()', '_fit()', 'predict()'
+                          ]
+                      )
+    graph.add_class(regLap)
+    graph.add_implementation(vBreg, Breg)
+    graph.add_implementation(eBreg, Breg)
+    graph.add_composition(vBreg, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    graph.add_composition(eBreg, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    graph.add_composition(omp, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    graph.add_composition(regArd, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    graph.add_composition(regLap, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    
+    print_and_save(graph, 'metamod_training')
+    
+def generate_training_uml_reduced():
+    """
+    Generates the uml for static training and related classes
+    """
+    graph = Graph('pyUML')
+    
+    # Add input class
+    inputs = UMLClass('Input')
+    graph.add_class(inputs)
+    inputspace = UMLClass('InputSpace',
+                          methods = ['build_polytypes()'])
+    graph.add_class(inputspace)
+    
+    expdesign = UMLClass('ExpDesigns',
+                         attributes = {
+                             'sampling_method': 'string',
+                             'hdf5_file': 'string',
+                             'n_init_samples': 'int',
+                             'out_dir': 'string'
+                             },
+                         methods = [
+                             'generate_samples()', 'generate_ED()', 
+                             'read_from_file()', 'random_sampler()',
+                             'pcm_sampler()', 'plot_samples()'
+                             ])
+    graph.add_class(expdesign)
+    graph.add_composition(inputs, expdesign, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    model = UMLClass('PyLinkForwardModel')
+    graph.add_class(model)
+    
+    metamod = UMLClass('MetaModel',
+                       attributes = {
+                           'input_obj':'Input',
+                           'meta_model_type': 'string',
+                           'pce_reg_method':'string',
+                           'bootstrap_method':'string',
+                           'n_bootstrap_itrs':'int',
+                           'pce_deg':'int',
+                           'pce_q_norm':'double',
+                           'dim_red_method':'string',
+                           #'apply_constraints':'bool',
+                           'verbose':'bool'
+                           },
+                       methods = [
+                           'build_metamodel()', 'fit()', 'update_pce_coeffs()',
+                           'add_InputSpace()', 'univ_basis_vals()', 'regression()',
+                           'adaptive_regression()', 'pca_transformation()', 'eval_metamodel()',
+                           'copy_meta_model_opts()', '_select_degree()', 
+                           'generate_polynomials()', '_compute_pce_moments()'
+                           ]
+                       )
+    graph.add_class(metamod)
+    graph.add_composition(inputs, metamod, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(inputspace, metamod, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    engine = UMLClass('Engine',
+                      attributes = {
+                          'MetaModel':'MetaModel',
+                          'Model':'Model',
+                          'ExpDesign':'ExpDesigns',
+                          'parallel': 'bool', 
+                          'trained':'bool',
+                          },
+                      methods = [
+                          'start_engine()', 'train_normal()',
+                          'eval_metamodel()', '_posteriorPlot()', '_validError()',
+                          '_error_Mean_Std()'
+                          ]
+                      )
+    graph.add_class(engine)
+    graph.add_composition(model, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(expdesign, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(metamod, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    
+    # Add the regression Classes
+    Breg = UMLClass('BayesianLinearRegression',
+                      attributes = {
+                          'n_iter':'int',
+                          'tol': 'double',
+                          'fit_intercept':'bool'
+                          },
+                      methods = [
+                          '_check_convergence()', '_center_data()', 'predict_dist()'
+                          ]
+                      )
+    graph.add_class(Breg)
+    vBreg = UMLClass('VBLinearRegression',
+                      methods = ['fit()', 'predict()']
+                      )
+    graph.add_class(vBreg)
+    eBreg = UMLClass('EBLinearRegression',
+                      methods = ['fit()', 'predict()']
+                      )
+    graph.add_class(eBreg)
+    omp = UMLClass('OrthogonalMatchingPursuit',
+                      attributes = {'fit_intercept':'bool'},
+                      methods = ['fit()', 'predict()']
+                      )
+    graph.add_class(omp)
+    regArd = UMLClass('RegressionFastARD',
+                      attributes = {
+                          'n_iter':'int',
+                          'tol':'double',
+                          'fit_intercept':'bool',
+                          'normalize':'bool',
+                          'compute_score':'bool'
+                          },
+                      methods = ['fit()', 'predict()']
+                      )
+    graph.add_class(regArd)
+    regLap = UMLClass('RegressionFastLaplace',
+                      attributes = {
+                          'n_iter':'int',
+                          'tol':'double',
+                          'fit_intercept':'bool',
+                          'bias_term':'bool'
+                          },
+                      methods = ['fit()', 'predict()']
+                      )
+    graph.add_class(regLap)
+    graph.add_implementation(vBreg, Breg)
+    graph.add_implementation(eBreg, Breg)
+    graph.add_composition(vBreg, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    graph.add_composition(eBreg, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    graph.add_composition(omp, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    graph.add_composition(regArd, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    graph.add_composition(regLap, metamod, multiplicity_parent = 1, multiplicity_child = '1..*')
+    
+    print_and_save(graph, 'metamod_training_reduced')
+    
+def generate_model_uml():
+    
+    graph = Graph('pyUML')
+    model = UMLClass('PyLinkForwardModel',
                      attributes = {
-                         'sampling_method': 'string',
-                         'hdf5_file': 'string',
-                         'n_new_samples': 'int',
-                         'n_max_samples': 'int',
-                         'mod_LOO_threshold': 'double',
-                         'tradeoff_scheme':'string',
-                         'n_canddidate': 'int',
-                         'explore_method':'string',
-                         'exploit_method': 'string',
-                         'util_func':'string',
-                         'n_cand_groups': 'int',
-                         'n_replication': 'int',
-                         'post_snapshot': 'bool', 
-                         'step_snapshot':'int',
-                         'max_a_post': 'list',
-                         'adapt_verbose':'bool',
-                         'max_func_itr': 'int'
+                         'link_type':'string', 
+                         'name':'string', 
+                         'py_file':'string',
+                        'func_args':'dict', 
+                        'shell_command':'string', 
+                        'input_file':'string',
+                        'input_template':'string', 
+                        'aux_file':'string', 
+                        'exe_path':'string',
+                        'output_file_names':'list', 
+                        'output_names':'list', 
+                        'output_parser':'string',
+                        'multi_process':'bool', 
+                        'n_cpus':'int', 
+                        'meas_file':'string',
+                        'meas_file_valid':'string', 
+                        'mc_ref_file':'string', 
+                        'obs_dict':'dict',
+                        'obs_dict_valid':'dict', 
+                        'mc_ref_dict':'dict'
                          },
                      methods = [
-                         'generate_samples()', 'generate_ED()', 
-                         'read_from_file()', 'random_sampler()',
-                         'pcm_sampler()'
-                         ])
-graph.add_class(expdesign)
-graph.add_implementation(expdesign, inputspace)
-
-print_and_save(graph, 'input_classes')
\ No newline at end of file
+                         'read_observation()', 'read_output()','update_input_params()',
+                         'run_command()', 'run_forwardmodel()', 'run_model_parallel()',
+                         'uMBridge_model()', '_store_simulations()', 'zip_subdirs()'
+                         ]
+                     )
+    graph.add_class(model)
+    print_and_save(graph, 'model')
+    
+def generate_al_uml():
+    """
+    Generates the uml for active learning
+    """
+    graph = Graph('pyUML')
+    
+    # Add input class
+    inputs = UMLClass('Input')
+    graph.add_class(inputs)
+    inputspace = UMLClass('InputSpace')
+    graph.add_class(inputspace)
+    
+    expdesign = UMLClass('ExpDesigns',
+                         attributes = {
+                             'sampling_method': 'string',
+                             'hdf5_file': 'string',
+                             'n_new_samples': 'int',
+                             'n_max_samples': 'int',
+                             'mod_LOO_threshold': 'double',
+                             'tradeoff_scheme':'string',
+                             'n_canddidate': 'int',
+                             'explore_method':'string',
+                             'exploit_method': 'string',
+                             'util_func':'string',
+                             'n_cand_groups': 'int',
+                             'n_replication': 'int',
+                             'post_snapshot': 'bool', 
+                             'step_snapshot':'int',
+                             'max_a_post': 'list',
+                             'adapt_verbose':'bool',
+                             'max_func_itr': 'int',
+                             'out_dir': 'string'
+                             },
+                         methods = [
+                             'generate_samples()', 'generate_ED()', 
+                             'read_from_file()', 'random_sampler()',
+                             'pcm_sampler()', 'plot_samples()'
+                             ])
+    graph.add_class(expdesign)
+    graph.add_composition(inputs, expdesign, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    model = UMLClass('PyLinkForwardModel',
+                     attributes = {
+                         'link_type':'string', 
+                         'name':'string', 
+                         'py_file':'string',
+                        'func_args':'dict', 
+                        'shell_command':'string', 
+                        'input_file':'string',
+                        'input_template':'string', 
+                        'aux_file':'string', 
+                        'exe_path':'string',
+                        'output_file_names':'list', 
+                        'output_names':'list', 
+                        'output_parser':'string',
+                        'multi_process':'bool', 
+                        'n_cpus':'int', 
+                        'meas_file':'string',
+                        'meas_file_valid':'string', 
+                        'mc_ref_file':'string', 
+                        'obs_dict':'dict',
+                        'obs_dict_valid':'dict', 
+                        'mc_ref_dict':'dict'
+                         },
+                     methods = [
+                         'read_observation()', 'read_output()','update_input_params()',
+                         'run_command()', 'run_forwardmodel()', 'run_model_parallel()',
+                         'uMBridge_model()', '_store_simulations()', 'zip_subdirs()'
+                         ]
+                     )
+    graph.add_class(model)
+    
+    metamod = UMLClass('MetaModel',
+                       attributes = {
+                           'input_obj':'Input',
+                           'meta_model_type': 'string',
+                           'pce_reg_method':'string',
+                           'bootstrap_method':'string',
+                           'n_bootstrap_itrs':'int',
+                           'pce_deg':'int',
+                           'pce_q_norm':'double',
+                           'dim_red_method':'string',
+                           'apply_constraints':'bool',
+                           'verbose':'bool'
+                           },
+                       methods = [
+                           'build_metamodel()', 'fit()', 'update_pce_coeffs()',
+                           'add_InputSpace()', 'univ_basis_vals()', 'regression()',
+                           'adaptive_regression()', 'pca_transformation()', 'eval_metamodel()',
+                           'copy_meta_model_opts()', 
+                           '_select_degree()', 'generate_polynomials()', '_compute_pce_moments()'
+                           ]
+                       )
+    graph.add_class(metamod)
+    graph.add_composition(inputs, metamod, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(inputspace, metamod, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    engine = UMLClass('Engine',
+                      attributes = {
+                          'MetaModel':'MetaModel',
+                          'Model':'Model',
+                          'ExpDesign':'ExpDesigns',
+                          'parallel': 'bool', 
+                          'trained':'bool',
+                          },
+                      methods = [
+                          'start_engine()', 'train_normal()', 'train_sequential()', 
+                          'eval_metamodel()', 'train_seq_design()', 'util_VarBasedDesign()', 
+                          'util_BayesianActiveDesign()', 'util_BayesianDesign()', 'run_util_func()', 
+                          'dual_annealing()', 'tradeoff_weights()', 'choose_next_sample()', 
+                          'util_AlphOptDesign()', '_normpdt()', '_corr_factor_BME()', 
+                          '_posteriorPlot()', '_BME_Calculator()', '_validError()', '_error_Mean_Std()'
+                          ]
+                      )
+    graph.add_class(engine)
+    graph.add_composition(model, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(expdesign, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(metamod, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    
+    explor = UMLClass('Exploration',
+                      attributes = {
+                          'ExpDesign':'ExpDesigns',
+                          'n_candidate':'int',
+                          'mc_criterion':'string',
+                          },
+                      methods=[
+                          'get_exploration_samples()','get_vornoi_samples()',
+                          'get_mc_samples()', 'approximate_voronoi()', '_build_dist_matrix_point()'
+                          ]
+                      )
+    graph.add_class(explor)
+    graph.add_composition(expdesign, explor, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(explor, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    
+    disc = UMLClass('Discrepancy',
+                    attributes = {
+                        'InputDosc':'Input',
+                        'disc_type':'string',
+                        'parameters':'list'
+                        },
+                    methods = [
+                        'get_sample()'
+                        ]
+                    )
+    graph.add_class(disc)
+    graph.add_composition(expdesign, disc, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(inputs, disc, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(disc, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    print_and_save(graph, 'active_learning')
+    
+       
+def generate_al_uml_reduced():
+    """
+    Generates the uml for active learning
+    """
+    graph = Graph('pyUML')
+    
+    # Add input class
+    inputs = UMLClass('Input')
+    graph.add_class(inputs)
+    
+    expdesign = UMLClass('ExpDesigns',
+                         attributes = {
+                             'sampling_method': 'string',
+                             'hdf5_file': 'string',
+                             'n_new_samples': 'int',
+                             'n_max_samples': 'int',
+                             'mod_LOO_threshold': 'double',
+                             'tradeoff_scheme':'string',
+                             'n_canddidate': 'int',
+                             'explore_method':'string',
+                             'exploit_method': 'string',
+                             'util_func':'string',
+                             'n_cand_groups': 'int',
+                             'n_replication': 'int',
+                             'post_snapshot': 'bool', 
+                             'step_snapshot':'int',
+                             'max_a_post': 'list',
+                             'adapt_verbose':'bool',
+                             'max_func_itr': 'int',
+                             'out_dir': 'string'
+                             },
+                         methods = [
+                             'generate_samples()', 'generate_ED()', 
+                             'read_from_file()', 'random_sampler()',
+                             'pcm_sampler()', 'plot_samples()'
+                             ])
+    graph.add_class(expdesign)
+    
+    model = UMLClass('PyLinkForwardModel')
+    graph.add_class(model)
+    
+    metamod = UMLClass('MetaModel')
+    graph.add_class(metamod)
+    
+    engine = UMLClass('Engine',
+                      attributes = {
+                          'MetaModel':'MetaModel',
+                          'Model':'Model',
+                          'ExpDesign':'ExpDesigns',
+                          'parallel': 'bool', 
+                          'trained':'bool',
+                          },
+                      methods = [
+                          'start_engine()', 'train_normal()', 'train_sequential()', 
+                          'eval_metamodel()', 'train_seq_design()', 'util_VarBasedDesign()', 
+                          'util_BayesianActiveDesign()', 'util_BayesianDesign()', 'run_util_func()', 
+                          'dual_annealing()', 'tradeoff_weights()', 'choose_next_sample()', 
+                          'util_AlphOptDesign()', '_normpdt()', '_corr_factor_BME()', 
+                          '_posteriorPlot()', '_BME_Calculator()', '_validError()', '_error_Mean_Std()'
+                          ]
+                      )
+    graph.add_class(engine)
+    graph.add_composition(model, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(expdesign, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(metamod, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    
+    explor = UMLClass('Exploration',
+                      attributes = {
+                          'ExpDesign':'ExpDesigns',
+                          'n_candidate':'int',
+                          'mc_criterion':'string',
+                          },
+                      methods=[
+                          'get_exploration_samples()','get_vornoi_samples()',
+                          'get_mc_samples()', 'approximate_voronoi()', '_build_dist_matrix_point()'
+                          ]
+                      )
+    graph.add_class(explor)
+    graph.add_composition(expdesign, explor, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(explor, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    disc = UMLClass('Discrepancy',
+                    attributes = {
+                        'InputDisc':'Input',
+                        'disc_type':'string',
+                        'parameters':'list'
+                        },
+                    methods = [
+                        'get_sample()'
+                        ]
+                    )
+    graph.add_class(disc)
+    graph.add_composition(expdesign, disc, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(inputs, disc, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(disc, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    print_and_save(graph, 'active_learning_reduced')
+    
+def generate_postprocessing_uml():
+    graph = Graph('pyUML')
+    
+    expdesign = UMLClass('ExpDesigns')
+    graph.add_class(expdesign)
+    
+    model = UMLClass('PyLinkForwardModel')
+    graph.add_class(model)
+    
+    metamod = UMLClass('MetaModel')
+    graph.add_class(metamod)
+    
+    engine = UMLClass('Engine')
+    graph.add_class(engine)
+    graph.add_composition(model, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(expdesign, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(metamod, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    post = UMLClass('PostProcessing',
+                    attributes = {
+                        'engine':'Engine',
+                        'name':'string',
+                        'out_dir':'string' # TODO: this is somehow not in the code anymore??
+                        },
+                    methods = [
+                        'plot_moments()', 'valid_metamodel()', 'check_accuracy()',
+                        'plot_seq_design_diagnostics()', 'sobol_indices()', 
+                        'check_reg_quality()', 'eval_pce_model_3d()', 'comput_pce_moments()',
+                        '_get_sample()', '_eval_model()', '_plot_validation()', '_plot_validation_multi()'
+                        ]
+                    )
+    graph.add_class(post)
+    graph.add_composition(engine, post, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    print_and_save(graph, 'postprocessing')
+    
+def generate_bayes_uml():
+    graph = Graph('pyUML')
+    
+    expdesign = UMLClass('ExpDesigns')
+    graph.add_class(expdesign)
+    
+    model = UMLClass('PyLinkForwardModel')
+    graph.add_class(model)
+    
+    metamod = UMLClass('MetaModel')
+    graph.add_class(metamod)
+    
+    engine = UMLClass('Engine')
+    graph.add_class(engine)
+    graph.add_composition(model, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(expdesign, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(metamod, engine, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    disc = UMLClass('Discrepancy')
+    graph.add_class(disc)
+    
+    bayesinf = UMLClass('BayesInference',
+                        attributes = {
+                            'engine':'Engine',
+                            'discrepancy':'Discrepancy',
+                            'emulator':'bool',
+                            'name':'string',
+                            'bootatrap':'bool',
+                            'req_outputs':'list',
+                            'selected_indices':'list',
+                            'prior_samples':'array',
+                            'n_prior_samples':'int',
+                            'measured_data':'dict',
+                            'inference_method':'string',
+                            'mcmc_params':'dict',
+                            'bayes_loocv':'bool',
+                            'n_bootstrap_itrs':'int',
+                            'perturbed_data':'lsit',
+                            'bootstrap_noise':'double',
+                            'just_analysis':'bool',
+                            'valid_metrics':'list',
+                            'plot_post_pred':'bool',
+                            'plot_map_pred':'bool',
+                            'max_a_posteriori':'string',
+                            'corner_title_fmt':'string',
+                            'out_dir':'string',
+                            'bmc':'bool'
+                            },
+                        methods = [
+                            'setup_inference()','create_inference()','create_error_model()',
+                            'perform_bootstrap()', '_perturb_data()', '_eval_model()',
+                            'normpdf()', '_coor_Factor_BME()', '_rejection_sampling()',
+                            '_posterior_predictive()', '_plot_max_a_posteriori()',
+                            'plot_post_params()', 'plot_log_BME()', '_plot_post_predictive()'
+                            ]
+                        )
+    graph.add_class(bayesinf)
+    graph.add_composition(engine, bayesinf, multiplicity_parent = 1, multiplicity_child = 1)
+    graph.add_composition(disc, bayesinf, multiplicity_parent = 1, multiplicity_child = 1)
+    
+    print_and_save(graph, 'bayesian_validation')
+                        
+    
+     
+    
+if __name__ == '__main__':
+    
+    generate_input_uml()
+    generate_input_uml_reduced()
+    generate_model_uml()
+    generate_training_uml()
+    generate_training_uml_reduced()
+    generate_al_uml()
+    generate_al_uml_reduced()
+    generate_postprocessing_uml()
+    generate_bayes_uml()
\ No newline at end of file
diff --git a/docs/diagrams/input_classes.dot b/docs/diagrams/input_classes.dot
index fcf3204ae5e7ce606e2f444c29ecfc4041e7f743..c84d679a61155bf55f90e2c5d78e5c04154b2d2a 100644
--- a/docs/diagrams/input_classes.dot
+++ b/docs/diagrams/input_classes.dot
@@ -4,6 +4,6 @@ Marginal [label="{Marginal|+ name : string\l+ dist_type : string\l+ parameters :
 Input -> Marginal  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
 InputSpace [label="{InputSpace|+ input_obj : Input\l+ meta_Model_type : string\l|+ check_valid_inputs()\l+ init_param_space()\l+ build_polytypes()\l+ transform()\l}", shape=record];
 InputSpace -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
-ExpDesigns [label="{ExpDesigns|+ sampling_method : string\l+ hdf5_file : string\l+ n_new_samples : int\l+ n_max_samples : int\l+ mod_LOO_threshold : double\l+ tradeoff_scheme : string\l+ n_canddidate : int\l+ explore_method : string\l+ exploit_method : string\l+ util_func : string\l+ n_cand_groups : int\l+ n_replication : int\l+ post_snapshot : bool\l+ step_snapshot : int\l+ max_a_post : list\l+ adapt_verbose : bool\l+ max_func_itr : int\l|+ generate_samples()\l+ generate_ED()\l+ read_from_file()\l+ random_sampler()\l+ pcm_sampler()\l}", shape=record];
+ExpDesigns [label="{ExpDesigns|+ sampling_method : string\l+ hdf5_file : string\l+ n_new_samples : int\l+ n_max_samples : int\l+ mod_LOO_threshold : double\l+ tradeoff_scheme : string\l+ n_canddidate : int\l+ explore_method : string\l+ exploit_method : string\l+ util_func : string\l+ n_cand_groups : int\l+ n_replication : int\l+ post_snapshot : bool\l+ step_snapshot : int\l+ max_a_post : list\l+ adapt_verbose : bool\l+ max_func_itr : int\l+ out_dir : string\l|+ generate_samples()\l+ generate_ED()\l+ read_from_file()\l+ random_sampler()\l+ pcm_sampler()\l+ plot_samples()\l}", shape=record];
 InputSpace -> ExpDesigns  [arrowtail=onormal, dir=back];
 }
diff --git a/docs/diagrams/input_classes_reduced.dot b/docs/diagrams/input_classes_reduced.dot
new file mode 100644
index 0000000000000000000000000000000000000000..68631697610d903609ae6701b496c05257c7c02d
--- /dev/null
+++ b/docs/diagrams/input_classes_reduced.dot
@@ -0,0 +1,9 @@
+digraph pyUML {
+Input [label="{Input|+ Marginals : list\l+ Rosenblatt : bool\l|+ add_marginals()\l}", shape=record];
+Marginal [label="{Marginal|+ name : string\l+ dist_type : string\l+ parameters : list\l+ input_data : array\l+ moments : list\l|}", shape=record];
+Input -> Marginal  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+InputSpace [label="{InputSpace|+ input_obj : Input\l+ meta_Model_type : string\l|+ check_valid_inputs()\l+ init_param_space()\l+ transform()\l}", shape=record];
+InputSpace -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+ExpDesigns [label="{ExpDesigns|+ sampling_method : string\l+ hdf5_file : string\l+ out_dir : string\l|+ generate_samples()\l+ generate_ED()\l+ read_from_file()\l+ random_sampler()\l+ pcm_sampler()\l+ plot_samples()\l}", shape=record];
+InputSpace -> ExpDesigns  [arrowtail=onormal, dir=back];
+}
diff --git a/docs/diagrams/metamod_training.dot b/docs/diagrams/metamod_training.dot
new file mode 100644
index 0000000000000000000000000000000000000000..13e9bd969aeee8c66283f0e14d3b6913e44d1a7c
--- /dev/null
+++ b/docs/diagrams/metamod_training.dot
@@ -0,0 +1,27 @@
+digraph pyUML {
+Input [label="{Input||}", shape=record];
+InputSpace [label="{InputSpace||}", shape=record];
+ExpDesigns [label="{ExpDesigns|+ sampling_method : string\l+ hdf5_file : string\l+ n_new_samples : int\l+ n_max_samples : int\l+ mod_LOO_threshold : double\l+ tradeoff_scheme : string\l+ n_canddidate : int\l+ explore_method : string\l+ exploit_method : string\l+ util_func : string\l+ n_cand_groups : int\l+ n_replication : int\l+ post_snapshot : bool\l+ step_snapshot : int\l+ max_a_post : list\l+ adapt_verbose : bool\l+ max_func_itr : int\l+ out_dir : string\l|+ generate_samples()\l+ generate_ED()\l+ read_from_file()\l+ random_sampler()\l+ pcm_sampler()\l+ plot_samples()\l}", shape=record];
+ExpDesigns -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+PyLinkForwardModel [label="{PyLinkForwardModel|+ link_type : string\l+ name : string\l+ py_file : string\l+ func_args : dict\l+ shell_command : string\l+ input_file : string\l+ input_template : string\l+ aux_file : string\l+ exe_path : string\l+ output_file_names : list\l+ output_names : list\l+ output_parser : string\l+ multi_process : bool\l+ n_cpus : int\l+ meas_file : string\l+ meas_file_valid : string\l+ mc_ref_file : string\l+ obs_dict : dict\l+ obs_dict_valid : dict\l+ mc_ref_dict : dict\l|+ read_observation()\l+ read_output()\l+ update_input_params()\l+ run_command()\l+ run_forwardmodel()\l+ run_model_parallel()\l+ uMBridge_model()\l+ _store_simulations()\l+ zip_subdirs()\l}", shape=record];
+MetaModel [label="{MetaModel|+ input_obj : Input\l+ meta_model_type : string\l+ pce_reg_method : string\l+ bootstrap_method : string\l+ n_bootstrap_itrs : int\l+ pce_deg : int\l+ pce_q_norm : double\l+ dim_red_method : string\l+ apply_constraints : bool\l+ verbose : bool\l|+ build_metamodel()\l+ fit()\l+ update_pce_coeffs()\l+ add_InputSpace()\l+ univ_basis_vals()\l+ regression()\l+ adaptive_regression()\l+ pca_transformation()\l+ eval_metamodel()\l+ create_model_error()\l+ eval_model_error()\l+ copy_meta_model_opts()\l+ _select_degree()\l+ generate_polynomials()\l+ _compute_pce_moments()\l}", shape=record];
+MetaModel -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+MetaModel -> InputSpace  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine [label="{Engine|+ MetaModel : MetaModel\l+ Model : Model\l+ ExpDesign : ExpDesigns\l+ parallel : bool\l+ trained : bool\l|+ start_engine()\l+ train_normal()\l+ train_sequential()\l+ eval_metamodel()\l+ train_seq_design()\l+ util_VarBasedDesign()\l+ util_BayesianActiveDesign()\l+ util_BayesianDesign()\l+ run_util_func()\l+ dual_annealing()\l+ tradeoff_weights()\l+ choose_next_sample()\l+ util_AlphOptDesign()\l+ _normpdt()\l+ _corr_factor_BME()\l+ _posteriorPlot()\l+ _BME_Calculator()\l+ _validError()\l+ _error_Mean_Std()\l}", shape=record];
+Engine -> PyLinkForwardModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> MetaModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+BayesianLinearRegression [label="{BayesianLinearRegression|+ n_iter : int\l+ tol : double\l+ fit_intercept : bool\l+ copy_X : bool\l+ verbose : bool\l|+ _check_convergence()\l+ _center_data()\l+ predict_dist()\l}", shape=record];
+VBLinearRegression [label="{VBLinearRegression|+ optimizer : string\l+ alpha : double\l+ normalize : bool\l+ scores : list\l+ perfect_fit_tol : double\l|+ fit()\l+ predict()\l}", shape=record];
+EBLinearRegression [label="{EBLinearRegression|+ a : double\l+ b : double\l+ c : double\l+ d : double\l|+ fit()\l+ predict()\l+ postrior_weights()\l}", shape=record];
+OrthogonalMatchingPursuit [label="{OrthogonalMatchingPursuit|+ fit_intercept : bool\l+ normalize : bool\l+ copy_X : bool\l+ verbose : bool\l|+ _preprocess_data()\l+ fit()\l+ predict()\l+ loo_error()\l+ blockwise_inverse()\l}", shape=record];
+RegressionFastARD [label="{RegressionFastARD|+ n_iter : int\l+ start : list\l+ tol : double\l+ fit_intercept : bool\l+ normalize : bool\l+ copy_X : bool\l+ compute_score : bool\l+ verbose : bool\l|+ _preprocess_data()\l+ fit()\l+ log_marginal_like()\l+ predict()\l+ _posterior_dist()\l+ _sparsity_quality()\l}", shape=record];
+RegressionFastLaplace [label="{RegressionFastLaplace|+ n_iter : int\l+ n_Kfold : int\l+ tol : double\l+ fit_intercept : bool\l+ bias_term : bool\l+ copy_X : bool\l+ verbose : bool\l|+ _center_data()\l+ fit()\l+ _fit()\l+ predict()\l}", shape=record];
+BayesianLinearRegression -> VBLinearRegression  [arrowtail=onormal, dir=back];
+BayesianLinearRegression -> EBLinearRegression  [arrowtail=onormal, dir=back];
+MetaModel -> VBLinearRegression  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+MetaModel -> EBLinearRegression  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+MetaModel -> OrthogonalMatchingPursuit  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+MetaModel -> RegressionFastARD  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+MetaModel -> RegressionFastLaplace  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+}
diff --git a/docs/diagrams/metamod_training_reduced.dot b/docs/diagrams/metamod_training_reduced.dot
new file mode 100644
index 0000000000000000000000000000000000000000..683a65869062de429c04096a2de16596b45ba640
--- /dev/null
+++ b/docs/diagrams/metamod_training_reduced.dot
@@ -0,0 +1,27 @@
+digraph pyUML {
+Input [label="{Input||}", shape=record];
+InputSpace [label="{InputSpace||+ build_polytypes()\l}", shape=record];
+ExpDesigns [label="{ExpDesigns|+ sampling_method : string\l+ hdf5_file : string\l+ n_init_samples : int\l+ out_dir : string\l|+ generate_samples()\l+ generate_ED()\l+ read_from_file()\l+ random_sampler()\l+ pcm_sampler()\l+ plot_samples()\l}", shape=record];
+ExpDesigns -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+PyLinkForwardModel [label="{PyLinkForwardModel||}", shape=record];
+MetaModel [label="{MetaModel|+ input_obj : Input\l+ meta_model_type : string\l+ pce_reg_method : string\l+ bootstrap_method : string\l+ n_bootstrap_itrs : int\l+ pce_deg : int\l+ pce_q_norm : double\l+ dim_red_method : string\l+ verbose : bool\l|+ build_metamodel()\l+ fit()\l+ update_pce_coeffs()\l+ add_InputSpace()\l+ univ_basis_vals()\l+ regression()\l+ adaptive_regression()\l+ pca_transformation()\l+ eval_metamodel()\l+ copy_meta_model_opts()\l+ _select_degree()\l+ generate_polynomials()\l+ _compute_pce_moments()\l}", shape=record];
+MetaModel -> Input  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+MetaModel -> InputSpace  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine [label="{Engine|+ MetaModel : MetaModel\l+ Model : Model\l+ ExpDesign : ExpDesigns\l+ parallel : bool\l+ trained : bool\l|+ start_engine()\l+ train_normal()\l+ eval_metamodel()\l+ _posteriorPlot()\l+ _validError()\l+ _error_Mean_Std()\l}", shape=record];
+Engine -> PyLinkForwardModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> MetaModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+BayesianLinearRegression [label="{BayesianLinearRegression|+ n_iter : int\l+ tol : double\l+ fit_intercept : bool\l|+ _check_convergence()\l+ _center_data()\l+ predict_dist()\l}", shape=record];
+VBLinearRegression [label="{VBLinearRegression||+ fit()\l+ predict()\l}", shape=record];
+EBLinearRegression [label="{EBLinearRegression||+ fit()\l+ predict()\l}", shape=record];
+OrthogonalMatchingPursuit [label="{OrthogonalMatchingPursuit|+ fit_intercept : bool\l|+ fit()\l+ predict()\l}", shape=record];
+RegressionFastARD [label="{RegressionFastARD|+ n_iter : int\l+ tol : double\l+ fit_intercept : bool\l+ normalize : bool\l+ compute_score : bool\l|+ fit()\l+ predict()\l}", shape=record];
+RegressionFastLaplace [label="{RegressionFastLaplace|+ n_iter : int\l+ tol : double\l+ fit_intercept : bool\l+ bias_term : bool\l|+ fit()\l+ predict()\l}", shape=record];
+BayesianLinearRegression -> VBLinearRegression  [arrowtail=onormal, dir=back];
+BayesianLinearRegression -> EBLinearRegression  [arrowtail=onormal, dir=back];
+MetaModel -> VBLinearRegression  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+MetaModel -> EBLinearRegression  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+MetaModel -> OrthogonalMatchingPursuit  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+MetaModel -> RegressionFastARD  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+MetaModel -> RegressionFastLaplace  [arrowtail=diamond, dir=back, headlabel="\n1..*", taillabel="\n1"];
+}
diff --git a/docs/diagrams/model.dot b/docs/diagrams/model.dot
new file mode 100644
index 0000000000000000000000000000000000000000..9345ce522a052727fa681393814eae794c240c37
--- /dev/null
+++ b/docs/diagrams/model.dot
@@ -0,0 +1,3 @@
+digraph pyUML {
+PyLinkForwardModel [label="{PyLinkForwardModel|+ link_type : string\l+ name : string\l+ py_file : string\l+ func_args : dict\l+ shell_command : string\l+ input_file : string\l+ input_template : string\l+ aux_file : string\l+ exe_path : string\l+ output_file_names : list\l+ output_names : list\l+ output_parser : string\l+ multi_process : bool\l+ n_cpus : int\l+ meas_file : string\l+ meas_file_valid : string\l+ mc_ref_file : string\l+ obs_dict : dict\l+ obs_dict_valid : dict\l+ mc_ref_dict : dict\l|+ read_observation()\l+ read_output()\l+ update_input_params()\l+ run_command()\l+ run_forwardmodel()\l+ run_model_parallel()\l+ uMBridge_model()\l+ _store_simulations()\l+ zip_subdirs()\l}", shape=record];
+}
diff --git a/docs/diagrams/postprocessing.dot b/docs/diagrams/postprocessing.dot
new file mode 100644
index 0000000000000000000000000000000000000000..c73d5af0b61c00fb12d12a76e92005acdb6000f2
--- /dev/null
+++ b/docs/diagrams/postprocessing.dot
@@ -0,0 +1,11 @@
+digraph pyUML {
+ExpDesigns [label="{ExpDesigns||}", shape=record];
+PyLinkForwardModel [label="{PyLinkForwardModel||}", shape=record];
+MetaModel [label="{MetaModel||}", shape=record];
+Engine [label="{Engine||}", shape=record];
+Engine -> PyLinkForwardModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> ExpDesigns  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+Engine -> MetaModel  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+PostProcessing [label="{PostProcessing|+ engine : Engine\l+ name : string\l+ out_dir : string\l|+ plot_moments()\l+ valid_metamodel()\l+ check_accuracy()\l+ plot_seq_design_diagnostics()\l+ sobol_indices()\l+ check_reg_quality()\l+ eval_pce_model_3d()\l+ comput_pce_moments()\l+ _get_sample()\l+ _eval_model()\l+ _plot_validation()\l+ _plot_validation_multi()\l}", shape=record];
+PostProcessing -> Engine  [arrowtail=diamond, dir=back, headlabel="\n1", taillabel="\n1"];
+}
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst
index 87376d6c45519e53f1323a61d30def9c57adb745..07efe980f7e7869f5b1c240585d44113c5c0765f 100644
--- a/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.exp\_designs.ExpDesigns
+bayesvalidrox.surrogate\_models.exp\_designs.ExpDesigns
 =======================================================
 
 .. currentmodule:: bayesvalidrox.surrogate_models.exp_designs
@@ -23,6 +23,7 @@ bayesvalidrox.surrogate\_models.exp\_designs.ExpDesigns
       ~ExpDesigns.generate_samples
       ~ExpDesigns.init_param_space
       ~ExpDesigns.pcm_sampler
+      ~ExpDesigns.plot_samples
       ~ExpDesigns.random_sampler
       ~ExpDesigns.read_from_file
       ~ExpDesigns.transform
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 028757fe8f8d768052679694c90236ebd9b63be6..dd9bb06fb0d2e92d10a644de1f03f8bd6e112265 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -23,7 +23,10 @@ Links
 Installation
 ------------
 This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on, use pip to install:
- $ pip install bayesvalidrox
+
+.. code-block:: bash
+
+   pip install bayesvalidrox
 
 #TODO Note other needed installations and tips
 
@@ -45,11 +48,15 @@ Further contents
 .. toctree::
    :maxdepth: 2
    
+   packagedescription
    tutorial
    examples
-   packagedescription
    api
    
+Contact
+-------
+#TODO Add options here
+   
    
 Indices and tables
 ==================
diff --git a/docs/source/input_description.rst b/docs/source/input_description.rst
index 27b97a81c51fc56ed48ebd970be05062490f1e2f..ee506fc23487557fe054015d11cc5e09c0ab0bf2 100644
--- a/docs/source/input_description.rst
+++ b/docs/source/input_description.rst
@@ -1,15 +1,13 @@
 Priors, input space and experimental design
 *******************************************
-.. note::
-	#TODO Write a short intro to uncertain inputs and sampling 
+The surrogate models, as used in BayesValidRox, consider model formulations where at least one of the input parameters is associated with uncertainty.
+This uncertainty can be described as probability distributions over possible values for the parameter.
 
-Input classes
-=============
 .. container:: twocol
 
    .. container:: leftside
    
-      Four classes contained in bayesvalidrox are associated with the inputs: :any:`bayesvalidrox.surrogate_models.inputs.Marginal`, :any:`bayesvalidrox.surrogate_models.inputs.Input`, :any:`bayesvalidrox.surrogate_models.input_space.InputSpace` and :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns`. 
+      Four classes contained in bayesvalidrox are associated with describing uncertain inputs: :any:`bayesvalidrox.surrogate_models.inputs.Marginal`, :any:`bayesvalidrox.surrogate_models.inputs.Input`, :any:`bayesvalidrox.surrogate_models.input_space.InputSpace` and :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns`. 
    
       Uncertain parameters are specified via their marginal distributions in :any:`bayesvalidrox.surrogate_models.inputs.Marginal` objects as either distribution types with associated parameters, or via a set of realizations.
       Supported distribution types include ``unif``, ``norm``, ``gamma``, ``beta``, ``lognorm``, ``expon`` and ``weibull``.
@@ -22,7 +20,7 @@ Input classes
    
       .. image:: ../diagrams/input_classes.png
          :width: 300
-         :alt: UML for input-related classes in bayesvalidrox
+         :alt: UML diagram for input-related classes in bayesvalidrox
    
 .. note::
    When using a polynomial-type surrogate setting ``rosenblatt`` to ``True`` results in all hermite polynomials.
@@ -58,15 +56,26 @@ If they are defined via distribution types, the ``name``, ``dist_type`` and ``pa
 
 If they are given via data, only ``name`` and ``input_data`` are relevant.
 
+>>> inputParams = np.random.uniform(-5,-5,100)
 >>> Inputs.add_marginals()
 >>> Inputs.Marginals[0].name = '$X$'
->>> Inputs.Marginals[0].input_data = inputParams[:, 0]
+>>> Inputs.Marginals[0].input_data = inputParams
 
 An experimental design can be constructed based on these inputs.
 
->>> ExpDesign = ExpDesign(Inputs)
+>>> ExpDesign = ExpDesigns(Inputs)
 
 Samples of the marginals can be created by specifying a sampling method and generating the wanted number of samples.
 
 >>> ExpDesign.sampling_method = 'latin_hypercube'
->>> samples = ExpDesign.generate_samples(100)
\ No newline at end of file
+>>> samples = ExpDesign.generate_samples(100)
+
+The generated samples can be visualized against their marginal distributions.
+
+>>> ExpDesign.plot_samples(samples)
+
+The results will be saved in the folder ``Outputs_Priors``.
+
+.. image:: ../../examples/user_guide/Outputs_Priors/prior_$X$.png
+   :width: 400
+   :alt: Generated samples against their marginal distribution
diff --git a/docs/source/packagedescription.rst b/docs/source/packagedescription.rst
index 33f94669090ebd964670c891f6af86d17e5eed4f..30164f68ce3cc9fab8eddc107552d004951a245c 100644
--- a/docs/source/packagedescription.rst
+++ b/docs/source/packagedescription.rst
@@ -1,38 +1,74 @@
 USER GUIDE
 **********
 
-Introductory theory
-===================
-.. note::
-   #TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.
+Installation
+------------
+BayesValidRox provides functionalities for describing uncertain parameters, building surrogate models based on model outputs and evaluating them with Bayesian validation methods.
+
+This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on.
+It can be installed with pip, best practice is to do so inside a virtual environment.
+
+.. code-block:: bash
+
+   python3 -m venv bayes_env
+   cd bayes_env
+   source bin/activate
+
+Here replace ``bayes_env`` with your preferred name.
+Then install the latest release of BayesValidRox inside the venv.
+
+.. code-block:: bash
+
+   pip install bayesvalidrox
 
+The current master can be installed by cloning the repository.
+
+.. code-block:: bash
+
+   git clone https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox.git
+   cd bayesvalidrox
+   pip install .
 
 Overview
 ========
-This package is split into multiple topics corresponding to the folder structure of the package.
+.. note::
+   #TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.
+
+This package is split into multiple aspects corresponding to its folder structure.
 
 
 .. image:: ../diagrams/folder_structure.png
    :width: 600
    :alt: Folder structure of **bayesvalidrox**
 
-The folder `surrogate_models` contains all the functions and classes that are necessary in order to create and train the surrogate model.
+The folder ``surrogate_models`` contains all the functions and classes that are necessary in order to create and train the surrogate model.
 This includes 
 
 * defining the input marginals
 * setting properties of the sampling in an experimental design
 * choosing the surrogate model and its properties
-* training the surrogate model on model evaluations in a straightforward or iterative manner
+* training the surrogate model on model evaluations in a straightforward manner or iteratively with active learning
+
+The computational model is linked via a ``pylink`` interface.
+
+Multiple post-processing options are available, including the calculation of Sobol' indices, checking the accuracy of the surrogate model and visualizations of the moments of the surrogate.
 
-The computational model is linked via a *pylink* interface.
-We split this into the aspects :any:`input_description` and :any:`surrogate_description` to provide insight into the options available in bayesvalidrox.
+Bayesian inference can be performed with rejection sampling or MCMC, while taking into account the estimated uncertainty of the data that the (surrogate) model is compared to.
+If multiple (surrogate) models are given, they can be compared against each other with pairwise Bayes Factors, model weights or a justifiability analysis.
 
-:any:`post_description` can be applied to trained surrogate models, or using the underlying models themselves.
+..
+   We split this into the aspects :any:`input_description` and :any:`surrogate_description` to provide insight into the options available in bayesvalidrox.
+..
+   :any:`post_description` can be applied to trained surrogate models, or using the underlying models themselves.
 
+The next pages lead through the topics given in BayesValidRox and describe the available classes and give brief examples for their use.
 
 .. toctree::
    :maxdepth: 1
    
    input_description
+   model_description
    surrogate_description
+   al_description
    post_description
+   bayes_description
diff --git a/examples/user_guide/example_user_guide.py b/examples/user_guide/example_user_guide.py
new file mode 100644
index 0000000000000000000000000000000000000000..563850ddcc1806ac0e3f3e3b23ee4a067a0b428a
--- /dev/null
+++ b/examples/user_guide/example_user_guide.py
@@ -0,0 +1,87 @@
+# -*- coding: utf-8 -*-
+"""
+Code that goes along with the 'user guide' in the BVR docs.
+
+@author: Rebecca Kohlhaas
+"""
+
+import numpy as np
+import pandas as pd
+import sys
+import joblib
+import matplotlib
+#matplotlib.use('agg')
+
+# Add BayesValidRox path
+sys.path.append("../../src/")
+from bayesvalidrox.surrogate_models.inputs import Input
+from bayesvalidrox.surrogate_models.exp_designs import ExpDesigns
+from bayesvalidrox.pylink.pylink import PyLinkForwardModel
+from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
+from bayesvalidrox.surrogate_models.engine import Engine
+from bayesvalidrox.post_processing.post_processing import PostProcessing
+
+if __name__ == '__main__':
+    #### Priors, input space and experimental design
+    version = 1
+    Inputs = Input()
+    
+    # Version 1
+    if version == 1:
+        Inputs.add_marginals()
+        Inputs.Marginals[0].name = '$X$'    
+        Inputs.Marginals[0].dist_type = 'unif'
+        Inputs.Marginals[0].parameters =  [-5, 5]
+        
+    # Version 2
+    if version == 2:
+        inputParams = np.random.uniform(-5,5,100)
+        Inputs.add_marginals()
+        Inputs.Marginals[0].name = '$X$'
+        Inputs.Marginals[0].input_data = inputParams
+
+    # Create ExpDesign and generate samples
+    ExpDesign = ExpDesigns(Inputs)
+    ExpDesign.sampling_method = 'latin_hypercube'
+    samples = ExpDesign.generate_samples(10)
+    ExpDesign.plot_samples(samples)
+    
+    #### Models
+    Model = PyLinkForwardModel()
+    Model.link_type = 'Function'
+    Model.py_file = 'model'
+    Model.name = 'model'
+    Model.Output.names = ['A', 'B']
+    
+    #output, samples = Model.run_model_parallel(samples, mp = True)
+    
+    #from model import model
+    #out1 = model(samples)
+    
+    #### Training surrogate models
+    MetaMod = MetaModel(Inputs)
+    MetaMod.meta_model_type = 'aPCE'
+    MetaMod.pce_reg_method = 'FastARD'
+    MetaMod.pce_deg = 3
+    MetaMod.pce_q_norm = 0.85
+    
+    ExpDesign.method = 'normal'
+    ExpDesign.n_init_samples = 10
+    ExpDesign.sampling_method = 'user'
+    ExpDesign.X = samples
+    
+    Engine_ = Engine(MetaMod, Model, ExpDesign)
+    Engine_.start_engine()
+    Engine_.train_normal()
+    
+    mean, stdev = Engine_.eval_metamodel(nsamples = 10)
+    mean, stdev = Engine_.MetaModel.eval_metamodel(samples)
+    
+    
+    #### Postprocessing
+    PostProc = PostProcessing(Engine_)
+    PostProc.valid_metamodel(n_samples=1)
+    PostProc.check_accuracy(n_samples=10)
+    PostProc.plot_moments()
+    PostProc.sobol_indices()
+    PostProc.plot_seq_design_diagnostics()
\ No newline at end of file
diff --git a/src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py b/src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py
index 96ef9c1d50b10b587ad0846d41733fc7f1cedfe8..45ff04a381f06df4c80633e239e5a7c82bebfe56 100644
--- a/src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py
+++ b/src/bayesvalidrox/surrogate_models/orthogonal_matching_pursuit.py
@@ -344,6 +344,7 @@ class OrthogonalMatchingPursuit(LinearModel, RegressorMixin):
             Inverse of the information matrix.
 
         """
+        # TODO: this can be transformed into an independent function
         if np.isscalar(D):
             # Inverse of D
             Dinv = 1/D