From f005940cd5df954b1e9c60acf27e2ea6e9ea47ef Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Fri, 28 Jun 2024 17:15:39 +0200
Subject: [PATCH] Added quickstart example and MIT license

---
 ...es_linear.BayesianLinearRegression.doctree | Bin 76258 -> 76316 bytes
 ...ls.bayes_linear.EBLinearRegression.doctree | Bin 116229 -> 116313 bytes
 ...ls.bayes_linear.VBLinearRegression.doctree | Bin 114999 -> 115083 bytes
 ..._pursuit.OrthogonalMatchingPursuit.doctree | Bin 102595 -> 102653 bytes
 ...els.reg_fast_ard.RegressionFastARD.doctree | Bin 114833 -> 114917 bytes
 docs/build/doctrees/al_description.doctree    | Bin 8922 -> 13400 bytes
 docs/build/doctrees/environment.pickle        | Bin 3081345 -> 3086385 bytes
 docs/build/doctrees/index.doctree             | Bin 13835 -> 23144 bytes
 .../doctrees/surrogate_description.doctree    | Bin 24802 -> 23776 bytes
 ...bayes_linear.BayesianLinearRegression.html |   2 +-
 ...odels.bayes_linear.EBLinearRegression.html |   4 +-
 ...odels.bayes_linear.VBLinearRegression.html |   4 +-
 ...ing_pursuit.OrthogonalMatchingPursuit.html |   2 +-
 ...models.reg_fast_ard.RegressionFastARD.html |   4 +-
 .../html/_sources/al_description.rst.txt      |  32 +++++++++
 docs/build/html/_sources/index.rst.txt        |  66 +++++++++++++++++-
 .../_sources/surrogate_description.rst.txt    |   7 +-
 docs/build/html/al_description.html           |  31 ++++++++
 docs/build/html/index.html                    |  65 ++++++++++++++++-
 docs/build/html/searchindex.js                |   2 +-
 docs/build/html/surrogate_description.html    |  10 +--
 docs/source/al_description.rst                |   6 +-
 docs/source/index.rst                         |  66 +++++++++++++++++-
 docs/source/surrogate_description.rst         |   7 +-
 examples/user_guide/example_user_guide.py     |   8 +--
 25 files changed, 273 insertions(+), 43 deletions(-)

diff --git a/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.doctree b/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.doctree
index ca8e3d341b301a696d16c49033e2b1c33e148c0b..bf9c982e7a3d6e9236bec4f10b0836d0ed0b142e 100644
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diff --git a/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.doctree b/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.doctree
index 457e7d87588347a81bd34014a2c3d8b87679fe4b..d2257d073b2a82c882476cc569e078fbd460a945 100644
GIT binary patch
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diff --git a/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.doctree b/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.doctree
index ab93deaf9f05d31fd30a581cd2c3440c0489e167..cdbcc3abe21abd2b6b4866453b5221873f9647ee 100644
GIT binary patch
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diff --git a/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.doctree b/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.doctree
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diff --git a/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.doctree b/docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.doctree
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diff --git a/docs/build/doctrees/al_description.doctree b/docs/build/doctrees/al_description.doctree
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diff --git a/docs/build/doctrees/index.doctree b/docs/build/doctrees/index.doctree
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diff --git a/docs/build/doctrees/surrogate_description.doctree b/docs/build/doctrees/surrogate_description.doctree
index 81898fef1fbbe5d6a6b76b051892a12dc74302fc..5c0a080e4afd1ccce3e8e0a20643c10ca2225ded 100644
GIT binary patch
delta 477
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html
index f0262935e..e3f16fb5c 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html
@@ -565,7 +565,7 @@ parameters and not others.</p>
 <p class="admonition-title">Note</p>
 <p>This method is only relevant if this estimator is used as a
 sub-estimator of a meta-estimator, e.g. used inside a
-<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
 </div>
 <section id="id12">
 <h2>Parameters<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h2>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html
index faf9eaf24..1e19d3665 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html
@@ -633,7 +633,7 @@ parameters and not others.</p>
 <p class="admonition-title">Note</p>
 <p>This method is only relevant if this estimator is used as a
 sub-estimator of a meta-estimator, e.g. used inside a
-<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
 </div>
 <section id="id15">
 <h3>Parameters<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
@@ -676,7 +676,7 @@ parameters and not others.</p>
 <p class="admonition-title">Note</p>
 <p>This method is only relevant if this estimator is used as a
 sub-estimator of a meta-estimator, e.g. used inside a
-<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
 </div>
 <section id="id17">
 <h3>Parameters<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html
index 71a0340bb..3ba8a66cb 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html
@@ -629,7 +629,7 @@ parameters and not others.</p>
 <p class="admonition-title">Note</p>
 <p>This method is only relevant if this estimator is used as a
 sub-estimator of a meta-estimator, e.g. used inside a
-<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
 </div>
 <section id="id15">
 <h3>Parameters<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
@@ -672,7 +672,7 @@ parameters and not others.</p>
 <p class="admonition-title">Note</p>
 <p>This method is only relevant if this estimator is used as a
 sub-estimator of a meta-estimator, e.g. used inside a
-<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
 </div>
 <section id="id17">
 <h3>Parameters<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html
index 3e5c7122e..43f20bf42 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html
@@ -650,7 +650,7 @@ parameters and not others.</p>
 <p class="admonition-title">Note</p>
 <p>This method is only relevant if this estimator is used as a
 sub-estimator of a meta-estimator, e.g. used inside a
-<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
 </div>
 <section id="id17">
 <h3>Parameters<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html
index 0ff5711b1..35ea14b81 100644
--- a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html
+++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html
@@ -632,7 +632,7 @@ parameters and not others.</p>
 <p class="admonition-title">Note</p>
 <p>This method is only relevant if this estimator is used as a
 sub-estimator of a meta-estimator, e.g. used inside a
-<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
 </div>
 <section id="id14">
 <h3>Parameters<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
@@ -675,7 +675,7 @@ parameters and not others.</p>
 <p class="admonition-title">Note</p>
 <p>This method is only relevant if this estimator is used as a
 sub-estimator of a meta-estimator, e.g. used inside a
-<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
 </div>
 <section id="id16">
 <h3>Parameters<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
diff --git a/docs/build/html/_sources/al_description.rst.txt b/docs/build/html/_sources/al_description.rst.txt
index c1d5e99f2..ac52ef0a6 100644
--- a/docs/build/html/_sources/al_description.rst.txt
+++ b/docs/build/html/_sources/al_description.rst.txt
@@ -25,3 +25,35 @@ The tradeoff between exploration and exploitation is defined by **tradeoff-schem
 Example
 =======
 We take the engine from :any:`surrogate_description` and change the settings to perform sequential training.
+
+This mainly changes the experimental design.
+For this example we start with the 10 initial samples from :any:`surrogate_description` and increase them iteratively to the number of samples given in ``n_max_samples``.
+The parameter ``n_new_samples`` sets the number of new samples that are chosen in each iteration, while ``mod_LOO_threshold`` sets an additional stopping condition.
+
+>>> ExpDesign.n_max_samples = 14
+>>> ExpDesign.n_new_samples = 1
+>>> ExpDesign.mod_LOO_threshold = 1e-16
+    
+Here we do not set a ``tradeoff_scheme``. 
+This will result in all samples being chosen based on the exploration weights.
+
+>>> ExpDesign.tradeoff_scheme = None
+    
+As the proposed samples come from the exploration method, we still need to define this.
+	
+>>> ExpDesign.explore_method = 'random'
+>>> ExpDesign.n_canddidate = 1000
+>>> ExpDesign.n_cand_groups = 4
+    
+For the exploitation method we use a variance-based method, as no data is given.
+	
+>>> ExpDesign.exploit_method = 'VarOptDesign'
+>>> ExpDesign.util_func = 'EIGF'
+    
+Once all properties are set, we can assemble the engine and start it.
+This time we use ``train_sequential``.
+	
+>>> Engine_ = Engine(MetaMod, Model, ExpDesign)
+>>> Engine_.start_engine()
+>>> Engine_.train_sequential()
+    
\ No newline at end of file
diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
index b533a730c..8671dc83e 100644
--- a/docs/build/html/_sources/index.rst.txt
+++ b/docs/build/html/_sources/index.rst.txt
@@ -30,11 +30,73 @@ This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0
 
 Quickstart
 ----------
-#TODO A minimal example to get people started
+Here we show a minimal example to get started on working with BayesValidRox.
+The :any:`packagedescription` goes into more detail on the available options and proposed workflow.
+
+The central functionalities of BayesValidRox all depend on building an object of class ``Engine`` that includes an interface to a model and a definition of an input space and sampling option in the form of an ``ExpDesigns`` object.
+It can contain and build a surrogate model of class ``MetaModel``, but also functions without one.
+
+We import the needed classes in our main file ``main.py``.
+
+>>> from bayesvalidrox import PyLinkForwardModel, InputSpace, ExpDesigns, Engine, MetaModel
+
+Here we use a simple linear model. 
+This is defined in another python file in the same folder, here we call it ``model.py``.
+This file contains a python function that expects samples of two parameter and returns a linear combination of them.
+For a detailed description of the expected output format see :any:`model_description`.
+
+>>> def model(samples):
+>>> return {'Z':samples[:,0]+2*samples[:,1], 'x_values':[0]}
+
+With this we can create the interface to the model in ``main.py``.
+
+>>> model = PyLinkForwardModel()
+>>> model.link_type = 'Function'
+>>> model.py_file = 'model'
+>>> model.name = 'linear model'
+>>> model.Output.names = ['Z']
+
+We specify marginal distributions on the inputs in an object of class ``InputSpace`` and use this to build the experimental design.
+
+>>> inputs = InputSpace()
+>>> inputs.add_Marginals()
+>>> inputs.Marginals[0].name = 'input0'
+>>> inputs.Marginals[0].dist_type = 'unif'
+>>> inputs.Marginals[0].parameters = [0,1]
+>>> inputs.add_Marginals()
+>>> inputs.Marginals[1].name = 'input1'
+>>> inputs.Marginals[1].dist_type = 'unif'
+>>> inputs.Marginals[1].parameters = [0,1]
+
+>>> expdes = ExpDesigns(inputs)
+>>> expdes.sampling_method = 'random'
+
+If we do not want to build a surrogate model, we can define the engine from these objects.
+
+>>> engine = Engine(None, model, expdes)
+
+If we want to build a surrogate model, we create and object of class ``MetaModel`` and set its properties.
+Here we build an arbitrary Polynomial Chaos Expansion and train it on samples given by the experimental design and the model.
+
+>>> metamodel = MetaModel(Inputs)
+>>> metamodel.meta_model_type = 'aPCE'
+>>> metamodel.pce_reg_method = 'FastARD'
+>>> metamodel.pce_deg = 3
+>>> MetaMod.pce_q_norm = 0.85
+
+>>> expdes.n_init_samples = 10
+
+>>> engine = Engine(metamodel, model, expdes)
+>>> engine.start_engine()
+>>> engine.train_normal()
+
+The engine with the trained metamodel can now be used for postprocessing, Bayesian inference, of Bayesian model comparison.
 
 License
 -------
-#TODO Note the License under which BVR is released
+BayesValidRox is licensed under the MIT license_.
+
+.. _license: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/master/LICENCE.md
 
 Contribution
 ------------
diff --git a/docs/build/html/_sources/surrogate_description.rst.txt b/docs/build/html/_sources/surrogate_description.rst.txt
index 61d89e6a6..1055fccb8 100644
--- a/docs/build/html/_sources/surrogate_description.rst.txt
+++ b/docs/build/html/_sources/surrogate_description.rst.txt
@@ -74,13 +74,8 @@ This combination will give us a sparse aPCE.
 >>> MetaMod.pce_deg = 3
 >>> MetaMod.pce_q_norm = 0.85
 
-Before we start the actual training we need to set some training-related values for the experimental design.
-Most importantly we set the attribute ``method`` to 'normal' to indicate that no iterations are performed during the training and we set ``n_init_samples`` to our wanted number of training samples.
+Before we start the actual training we set ``n_init_samples`` to our wanted number of training samples.
 
-.. warning::
-   The attribute ``method`` will be deprecated in further release.
-   
->>> ExpDesign.method = 'normal'
 >>> ExpDesign.n_init_samples = 10
 
 Like this the experimental design will generate 10 samples according to our previously set sampling method.
diff --git a/docs/build/html/al_description.html b/docs/build/html/al_description.html
index d00aea23b..78a5f609e 100644
--- a/docs/build/html/al_description.html
+++ b/docs/build/html/al_description.html
@@ -356,6 +356,37 @@ Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning,
 <section id="example">
 <h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
 <p>We take the engine from <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a> and change the settings to perform sequential training.</p>
+<p>This mainly changes the experimental design.
+For this example we start with the 10 initial samples from <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a> and increase them iteratively to the number of samples given in <code class="docutils literal notranslate"><span class="pre">n_max_samples</span></code>.
+The parameter <code class="docutils literal notranslate"><span class="pre">n_new_samples</span></code> sets the number of new samples that are chosen in each iteration, while <code class="docutils literal notranslate"><span class="pre">mod_LOO_threshold</span></code> sets an additional stopping condition.</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">n_max_samples</span> <span class="o">=</span> <span class="mi">14</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_new_samples</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">mod_LOO_threshold</span> <span class="o">=</span> <span class="mf">1e-16</span>
+</pre></div>
+</div>
+<p>Here we do not set a <code class="docutils literal notranslate"><span class="pre">tradeoff_scheme</span></code>.
+This will result in all samples being chosen based on the exploration weights.</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">tradeoff_scheme</span> <span class="o">=</span> <span class="kc">None</span>
+</pre></div>
+</div>
+<p>As the proposed samples come from the exploration method, we still need to define this.</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">explore_method</span> <span class="o">=</span> <span class="s1">&#39;random&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_canddidate</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_cand_groups</span> <span class="o">=</span> <span class="mi">4</span>
+</pre></div>
+</div>
+<p>For the exploitation method we use a variance-based method, as no data is given.</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">exploit_method</span> <span class="o">=</span> <span class="s1">&#39;VarOptDesign&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">util_func</span> <span class="o">=</span> <span class="s1">&#39;EIGF&#39;</span>
+</pre></div>
+</div>
+<p>Once all properties are set, we can assemble the engine and start it.
+This time we use <code class="docutils literal notranslate"><span class="pre">train_sequential</span></code>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">Engine_</span> <span class="o">=</span> <span class="n">Engine</span><span class="p">(</span><span class="n">MetaMod</span><span class="p">,</span> <span class="n">Model</span><span class="p">,</span> <span class="n">ExpDesign</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Engine_</span><span class="o">.</span><span class="n">start_engine</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Engine_</span><span class="o">.</span><span class="n">train_sequential</span><span class="p">()</span>
+</pre></div>
+</div>
 </section>
 </section>
 
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index ac12863ce..460cf3cb7 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -358,11 +358,72 @@ The functionality and options for the different classes is described more in-dep
 </section>
 <section id="quickstart">
 <h2>Quickstart<a class="headerlink" href="#quickstart" title="Link to this heading">¶</a></h2>
-<p>#TODO A minimal example to get people started</p>
+<p>Here we show a minimal example to get started on working with BayesValidRox.
+The <a class="reference internal" href="packagedescription.html"><span class="doc">USER GUIDE</span></a> goes into more detail on the available options and proposed workflow.</p>
+<p>The central functionalities of BayesValidRox all depend on building an object of class <code class="docutils literal notranslate"><span class="pre">Engine</span></code> that includes an interface to a model and a definition of an input space and sampling option in the form of an <code class="docutils literal notranslate"><span class="pre">ExpDesigns</span></code> object.
+It can contain and build a surrogate model of class <code class="docutils literal notranslate"><span class="pre">MetaModel</span></code>, but also functions without one.</p>
+<p>We import the needed classes in our main file <code class="docutils literal notranslate"><span class="pre">main.py</span></code>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">bayesvalidrox</span> <span class="kn">import</span> <span class="n">PyLinkForwardModel</span><span class="p">,</span> <span class="n">InputSpace</span><span class="p">,</span> <span class="n">ExpDesigns</span><span class="p">,</span> <span class="n">Engine</span><span class="p">,</span> <span class="n">MetaModel</span>
+</pre></div>
+</div>
+<p>Here we use a simple linear model.
+This is defined in another python file in the same folder, here we call it <code class="docutils literal notranslate"><span class="pre">model.py</span></code>.
+This file contains a python function that expects samples of two parameter and returns a linear combination of them.
+For a detailed description of the expected output format see <a class="reference internal" href="model_description.html"><span class="doc">Models</span></a>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="n">samples</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">return</span> <span class="p">{</span><span class="s1">&#39;Z&#39;</span><span class="p">:</span><span class="n">samples</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">samples</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;x_values&#39;</span><span class="p">:[</span><span class="mi">0</span><span class="p">]}</span>
+</pre></div>
+</div>
+<p>With this we can create the interface to the model in <code class="docutils literal notranslate"><span class="pre">main.py</span></code>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">PyLinkForwardModel</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">link_type</span> <span class="o">=</span> <span class="s1">&#39;Function&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">py_file</span> <span class="o">=</span> <span class="s1">&#39;model&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;linear model&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">Output</span><span class="o">.</span><span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Z&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>We specify marginal distributions on the inputs in an object of class <code class="docutils literal notranslate"><span class="pre">InputSpace</span></code> and use this to build the experimental design.</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">InputSpace</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;input0&#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">dist_type</span> <span class="o">=</span> <span class="s1">&#39;unif&#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">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</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">1</span><span class="p">]</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;input1&#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">1</span><span class="p">]</span><span class="o">.</span><span class="n">dist_type</span> <span class="o">=</span> <span class="s1">&#39;unif&#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">1</span><span class="p">]</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">expdes</span> <span class="o">=</span> <span class="n">ExpDesigns</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">expdes</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">&#39;random&#39;</span>
+</pre></div>
+</div>
+<p>If we do not want to build a surrogate model, we can define the engine from these objects.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">engine</span> <span class="o">=</span> <span class="n">Engine</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">expdes</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>If we want to build a surrogate model, we create and object of class <code class="docutils literal notranslate"><span class="pre">MetaModel</span></code> and set its properties.
+Here we build an arbitrary Polynomial Chaos Expansion and train it on samples given by the experimental design and the model.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metamodel</span> <span class="o">=</span> <span class="n">MetaModel</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">metamodel</span><span class="o">.</span><span class="n">meta_model_type</span> <span class="o">=</span> <span class="s1">&#39;aPCE&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">metamodel</span><span class="o">.</span><span class="n">pce_reg_method</span> <span class="o">=</span> <span class="s1">&#39;FastARD&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">metamodel</span><span class="o">.</span><span class="n">pce_deg</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">MetaMod</span><span class="o">.</span><span class="n">pce_q_norm</span> <span class="o">=</span> <span class="mf">0.85</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">expdes</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">10</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">engine</span> <span class="o">=</span> <span class="n">Engine</span><span class="p">(</span><span class="n">metamodel</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">expdes</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">engine</span><span class="o">.</span><span class="n">start_engine</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">engine</span><span class="o">.</span><span class="n">train_normal</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>The engine with the trained metamodel can now be used for postprocessing, Bayesian inference, of Bayesian model comparison.</p>
 </section>
 <section id="license">
 <h2>License<a class="headerlink" href="#license" title="Link to this heading">¶</a></h2>
-<p>#TODO Note the License under which BVR is released</p>
+<p>BayesValidRox is licensed under the MIT <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/master/LICENCE.md">license</a>.</p>
 </section>
 <section id="contribution">
 <h2>Contribution<a class="headerlink" href="#contribution" title="Link to this heading">¶</a></h2>
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 649219d8b..cd3af9ca8 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"API": [[66, "api"]], "Active learning: iteratively expanding the training set": [[64, "active-learning-iteratively-expanding-the-training-set"]], "Arguments": [[29, "arguments"], [42, "arguments"], [42, "id2"]], "Attributes": [[3, "attributes"], [5, "attributes"], [7, "attributes"], [9, "attributes"], [13, "attributes"], [16, "attributes"], [25, "attributes"], [26, "attributes"], [39, "attributes"], [42, "attributes"], [47, "attributes"], [49, "attributes"], [50, "attributes"], [52, "attributes"], [55, "attributes"], [58, "attributes"], [60, "attributes"]], "Bayesian Inference": [[81, "bayesian-inference"]], "Bayesian inference and multi-model comparison": [[67, "bayesian-inference-and-multi-model-comparison"]], "Contribution": [[71, "contribution"]], "Define probabilistic input model": [[81, "define-probabilistic-input-model"]], "Define surrogate (meta) model": [[81, "define-surrogate-meta-model"]], "Define the data uncertainty": [[81, "define-the-data-uncertainty"]], "Define the model with PyLinkForwardModel": [[81, "define-the-model-with-pylinkforwardmodel"]], "Discrepancy": [[65, "id3"], [68, "id3"]], "EXAMPLES": [[70, "examples"]], "Example": [[64, "example"], [72, "example"], [74, "example"], [79, "example"], [80, "example"]], "Example: Analytical function": [[65, "example-analytical-function"]], "Example: OHagan-function": [[76, "example-ohagan-function"]], "Example: beam": [[68, "example-beam"]], "Example: borehole": [[69, "example-borehole"]], "Example: ishigami": [[73, "example-ishigami"]], "Example: model comparison": [[75, "example-model-comparison"]], "Example: pollution": [[78, "example-pollution"]], "Examples": [[49, "examples"]], "Exploration, exploitation and tradeoff": [[64, "exploration-exploitation-and-tradeoff"]], "Further contents": [[71, "further-contents"]], "Import necessary libraries": [[81, "import-necessary-libraries"]], "Indices and tables": [[71, "indices-and-tables"]], "Installation": [[71, "installation"], [77, "installation"]], "License": [[71, "license"]], "Links": [[71, "links"]], "MetaModel options": [[80, "metamodel-options"]], "MetaModel settings": [[65, "id4"], [68, "id4"], [69, "id3"], [73, "id3"], [75, "id7"], [76, "id3"], [78, "id3"]], "Model 1: L2_model": [[75, "model-1-l2-model"]], "Model 1: NL2_model": [[75, "model-1-nl2-model"]], "Model 1: NL4_model": [[75, "model-1-nl4-model"]], "Model and Data": [[65, "model-and-data"], [68, "model-and-data"], [69, "model-and-data"], [73, "model-and-data"], [76, "model-and-data"], [78, "model-and-data"]], "Models": [[74, "models"]], "Note": [[39, "note"], [60, "note"]], "Notes": [[24, "notes"], [25, "notes"], [26, "notes"], [52, "notes"], [55, "notes"]], "Overview": [[77, "overview"]], "Parameters": [[3, "parameters"], [3, "id2"], [3, "id4"], [3, "id7"], [5, "parameters"], [5, "id1"], [5, "id3"], [5, "id5"], [5, "id7"], [5, "id9"], [5, "id11"], [5, "id13"], [5, "id15"], [7, "parameters"], [9, "parameters"], [9, 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\ No newline at end of file
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"bayesvalidrox.bayes_inference", "bayesvalidrox.bayes_inference.bayes_inference", "bayesvalidrox.bayes_inference.bayes_inference.BayesInference", "bayesvalidrox.bayes_inference.bayes_model_comparison", "bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison", "bayesvalidrox.bayes_inference.discrepancy", "bayesvalidrox.bayes_inference.discrepancy.Discrepancy", "bayesvalidrox.bayes_inference.mcmc", "bayesvalidrox.bayes_inference.mcmc.MCMC", "bayesvalidrox.bayes_inference.mcmc.gelman_rubin", "bayesvalidrox.post_processing", "bayesvalidrox.post_processing.post_processing", "bayesvalidrox.post_processing.post_processing.PostProcessing", "bayesvalidrox.pylink", "bayesvalidrox.pylink.pylink", "bayesvalidrox.pylink.pylink.PyLinkForwardModel", "bayesvalidrox.pylink.pylink.within_range", "bayesvalidrox.surrogate_models", "bayesvalidrox.surrogate_models.adaptPlot", "bayesvalidrox.surrogate_models.adaptPlot.adaptPlot", "bayesvalidrox.surrogate_models.apoly_construction", "bayesvalidrox.surrogate_models.apoly_construction.apoly_construction", "bayesvalidrox.surrogate_models.bayes_linear", "bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression", "bayesvalidrox.surrogate_models.bayes_linear.gamma_mean", "bayesvalidrox.surrogate_models.engine", "bayesvalidrox.surrogate_models.engine.Engine", "bayesvalidrox.surrogate_models.engine.hellinger_distance", "bayesvalidrox.surrogate_models.engine.logpdf", "bayesvalidrox.surrogate_models.engine.subdomain", "bayesvalidrox.surrogate_models.eval_rec_rule", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary", "bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis", "bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs", "bayesvalidrox.surrogate_models.exp_designs", "bayesvalidrox.surrogate_models.exp_designs.ExpDesigns", "bayesvalidrox.surrogate_models.exp_designs.check_ranges", "bayesvalidrox.surrogate_models.exploration", "bayesvalidrox.surrogate_models.exploration.Exploration", "bayesvalidrox.surrogate_models.glexindex", "bayesvalidrox.surrogate_models.glexindex.cross_truncate", "bayesvalidrox.surrogate_models.glexindex.glexindex", "bayesvalidrox.surrogate_models.input_space", "bayesvalidrox.surrogate_models.input_space.InputSpace", "bayesvalidrox.surrogate_models.inputs", "bayesvalidrox.surrogate_models.inputs.Input", "bayesvalidrox.surrogate_models.inputs.Marginal", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit", "bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr", "bayesvalidrox.surrogate_models.reg_fast_ard", "bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD", 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"TUTORIAL"], "titleterms": {"1": 75, "3": 75, "activ": 64, "adaptplot": [19, 20], "also": [], "an": 81, "analyt": 65, "api": 66, "apoly_construct": [21, 22], "argument": [29, 42], "assist": 71, "attribut": [3, 5, 7, 9, 13, 16, 25, 26, 39, 42, 47, 49, 50, 52, 55, 58, 60], "bay": [], "bayes_infer": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "bayes_linear": [23, 24, 25, 26, 27], "bayes_model_comparison": [4, 5], "bayesian": [67, 71, 81], "bayesianlinearregress": 24, "bayesinfer": 3, "bayesmodelcomparison": 5, "bayesvalidrox": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], "beam": 68, "borehol": 69, "check_rang": 40, "choic": [65, 68, 69, 73, 75, 76, 78], "class": [], "comparison": [67, 75], "comput": 71, "contact": [], "content": 71, "contribut": 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, 64, 65, 68, 69, 70, 72, 73, 74, 75, 76, 78, 79, 80], "exp_design": [38, 39, 40], "expand": 64, "expdesign": 39, "experiment": [72, 81], "exploit": 64, "explor": [41, 42, 64], "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": 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30, 31, 32, 34, 35, 36, 37, 39, 40, 42, 47, 49, 52, 55, 58, 60, 61, 62, 63], "see": [], "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": [], "tradeoff": 64, "train": [64, 65, 68, 69, 73, 75, 76, 78, 80, 81], "tutori": 81, "uncertainti": 81, "update_precis": 56, "user": 77, "valid": 71, "vblinearregress": 26, "within_rang": 17}})
\ No newline at end of file
diff --git a/docs/build/html/surrogate_description.html b/docs/build/html/surrogate_description.html
index a9af86955..5067bcefc 100644
--- a/docs/build/html/surrogate_description.html
+++ b/docs/build/html/surrogate_description.html
@@ -395,14 +395,8 @@ This combination will give us a sparse aPCE.</p>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">MetaMod</span><span class="o">.</span><span class="n">pce_q_norm</span> <span class="o">=</span> <span class="mf">0.85</span>
 </pre></div>
 </div>
-<p>Before we start the actual training we need to set some training-related values for the experimental design.
-Most importantly we set the attribute <code class="docutils literal notranslate"><span class="pre">method</span></code> to ‘normal’ to indicate that no iterations are performed during the training and we set <code class="docutils literal notranslate"><span class="pre">n_init_samples</span></code> to our wanted number of training samples.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>The attribute <code class="docutils literal notranslate"><span class="pre">method</span></code> will be deprecated in further release.</p>
-</div>
-<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">method</span> <span class="o">=</span> <span class="s1">&#39;normal&#39;</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">10</span>
+<p>Before we start the actual training we set <code class="docutils literal notranslate"><span class="pre">n_init_samples</span></code> to our wanted number of training samples.</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">n_init_samples</span> <span class="o">=</span> <span class="mi">10</span>
 </pre></div>
 </div>
 <p>Like this the experimental design will generate 10 samples according to our previously set sampling method.
diff --git a/docs/source/al_description.rst b/docs/source/al_description.rst
index 7922be0f8..ac52ef0a6 100644
--- a/docs/source/al_description.rst
+++ b/docs/source/al_description.rst
@@ -26,12 +26,10 @@ Example
 =======
 We take the engine from :any:`surrogate_description` and change the settings to perform sequential training.
 
-This mainly changes the ``Engine``.
-We set the ``method`` to ``'sequential'`` to indicate that active learning should be performed.
-For this example we start with the 10 initial samples from :any:`surrogate_description` and increase this iteratively to the number of samples given in ``n_max_samples``.
+This mainly changes the experimental design.
+For this example we start with the 10 initial samples from :any:`surrogate_description` and increase them iteratively to the number of samples given in ``n_max_samples``.
 The parameter ``n_new_samples`` sets the number of new samples that are chosen in each iteration, while ``mod_LOO_threshold`` sets an additional stopping condition.
 
->>> ExpDesign.method = 'sequential'
 >>> ExpDesign.n_max_samples = 14
 >>> ExpDesign.n_new_samples = 1
 >>> ExpDesign.mod_LOO_threshold = 1e-16
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b533a730c..8671dc83e 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -30,11 +30,73 @@ This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0
 
 Quickstart
 ----------
-#TODO A minimal example to get people started
+Here we show a minimal example to get started on working with BayesValidRox.
+The :any:`packagedescription` goes into more detail on the available options and proposed workflow.
+
+The central functionalities of BayesValidRox all depend on building an object of class ``Engine`` that includes an interface to a model and a definition of an input space and sampling option in the form of an ``ExpDesigns`` object.
+It can contain and build a surrogate model of class ``MetaModel``, but also functions without one.
+
+We import the needed classes in our main file ``main.py``.
+
+>>> from bayesvalidrox import PyLinkForwardModel, InputSpace, ExpDesigns, Engine, MetaModel
+
+Here we use a simple linear model. 
+This is defined in another python file in the same folder, here we call it ``model.py``.
+This file contains a python function that expects samples of two parameter and returns a linear combination of them.
+For a detailed description of the expected output format see :any:`model_description`.
+
+>>> def model(samples):
+>>> return {'Z':samples[:,0]+2*samples[:,1], 'x_values':[0]}
+
+With this we can create the interface to the model in ``main.py``.
+
+>>> model = PyLinkForwardModel()
+>>> model.link_type = 'Function'
+>>> model.py_file = 'model'
+>>> model.name = 'linear model'
+>>> model.Output.names = ['Z']
+
+We specify marginal distributions on the inputs in an object of class ``InputSpace`` and use this to build the experimental design.
+
+>>> inputs = InputSpace()
+>>> inputs.add_Marginals()
+>>> inputs.Marginals[0].name = 'input0'
+>>> inputs.Marginals[0].dist_type = 'unif'
+>>> inputs.Marginals[0].parameters = [0,1]
+>>> inputs.add_Marginals()
+>>> inputs.Marginals[1].name = 'input1'
+>>> inputs.Marginals[1].dist_type = 'unif'
+>>> inputs.Marginals[1].parameters = [0,1]
+
+>>> expdes = ExpDesigns(inputs)
+>>> expdes.sampling_method = 'random'
+
+If we do not want to build a surrogate model, we can define the engine from these objects.
+
+>>> engine = Engine(None, model, expdes)
+
+If we want to build a surrogate model, we create and object of class ``MetaModel`` and set its properties.
+Here we build an arbitrary Polynomial Chaos Expansion and train it on samples given by the experimental design and the model.
+
+>>> metamodel = MetaModel(Inputs)
+>>> metamodel.meta_model_type = 'aPCE'
+>>> metamodel.pce_reg_method = 'FastARD'
+>>> metamodel.pce_deg = 3
+>>> MetaMod.pce_q_norm = 0.85
+
+>>> expdes.n_init_samples = 10
+
+>>> engine = Engine(metamodel, model, expdes)
+>>> engine.start_engine()
+>>> engine.train_normal()
+
+The engine with the trained metamodel can now be used for postprocessing, Bayesian inference, of Bayesian model comparison.
 
 License
 -------
-#TODO Note the License under which BVR is released
+BayesValidRox is licensed under the MIT license_.
+
+.. _license: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/master/LICENCE.md
 
 Contribution
 ------------
diff --git a/docs/source/surrogate_description.rst b/docs/source/surrogate_description.rst
index 61d89e6a6..1055fccb8 100644
--- a/docs/source/surrogate_description.rst
+++ b/docs/source/surrogate_description.rst
@@ -74,13 +74,8 @@ This combination will give us a sparse aPCE.
 >>> MetaMod.pce_deg = 3
 >>> MetaMod.pce_q_norm = 0.85
 
-Before we start the actual training we need to set some training-related values for the experimental design.
-Most importantly we set the attribute ``method`` to 'normal' to indicate that no iterations are performed during the training and we set ``n_init_samples`` to our wanted number of training samples.
+Before we start the actual training we set ``n_init_samples`` to our wanted number of training samples.
 
-.. warning::
-   The attribute ``method`` will be deprecated in further release.
-   
->>> ExpDesign.method = 'normal'
 >>> ExpDesign.n_init_samples = 10
 
 Like this the experimental design will generate 10 samples according to our previously set sampling method.
diff --git a/examples/user_guide/example_user_guide.py b/examples/user_guide/example_user_guide.py
index 0a1f5ed06..f118d05a6 100644
--- a/examples/user_guide/example_user_guide.py
+++ b/examples/user_guide/example_user_guide.py
@@ -65,7 +65,6 @@ if __name__ == '__main__':
     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
@@ -78,18 +77,19 @@ if __name__ == '__main__':
     mean, stdev = Engine_.MetaModel.eval_metamodel(samples)
     
     #### Active learning
-    ExpDesign.method = 'sequential'
-    
-    # Set the sampling parameters
     ExpDesign.n_new_samples = 1
     ExpDesign.n_max_samples = 14
     ExpDesign.mod_LOO_threshold = 1e-16
+    
     ExpDesign.tradeoff_scheme = None
+    
     ExpDesign.explore_method = 'random'
     ExpDesign.n_canddidate = 1000
     ExpDesign.n_cand_groups = 4
+    
     ExpDesign.exploit_method = 'VarOptDesign'
     ExpDesign.util_func = 'EIGF'
+    
     Engine_ = Engine(MetaMod, Model, ExpDesign)
     Engine_.start_engine()
     Engine_.train_sequential()
-- 
GitLab