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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 f0262935e671136434b3ace548117b639faaf866..e3f16fb5c421af8aef20e6ad48e17acc12af2cc3 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 faf9eaf24d776041650e1e6dcb3387562ea92652..1e19d36659a9f0e61251e9a2df51a7390a84e03a 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 71a0340bbb6f819f21890a5d5b8c1bf838020d4a..3ba8a66cb906826dceb78ad5e49f2025a5b042d7 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 3e5c7122e259e03f7696886326d870b01236c5dc..43f20bf429b54517e920f5cc4caad9ba94e71352 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 0ff5711b1197cef309e92dba0306360e8dd2302e..35ea14b81f33c21e5170f9ac5952fb87f1d38449 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 c1d5e99f2ec65aeaf8f0933096bd351ca0c6e465..ac52ef0a6d1fc427527c94dd991756aa04634ef5 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 b533a730c30cb41d03e9e2c20d0ef9683185f53d..8671dc83e43f77a6f5501c7f61e6a040eb5aa12a 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 61d89e6a6ff5bf1ffa27210ce1a077ac0634e8c9..1055fccb86df67983dd7f4d75c072b5dae989c7a 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 d00aea23bc8449a6c2ef71e173813d4df09573bf..78a5f609efb6ef18b9a5cf743e0927f79504fb15 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>
 
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index ac12863ce5183832f1a06829fdb056e17619fa1b..460cf3cb78de9b9cd652a03cf2a89b74f44b34af 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 649219d8b553a969734cc8d31b10ccee2dd1bf14..cd3af9ca8735687c9a3f1d1d50be88c9466e3569 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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"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": [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": [], "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 a9af8695565ac3e9e094942bcccbdcf924f298be..5067bcefc9a84dd6dafa11384999af1e0f878a90 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 7922be0f86155e5fc39211672c03120fa1552cf9..ac52ef0a6d1fc427527c94dd991756aa04634ef5 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 b533a730c30cb41d03e9e2c20d0ef9683185f53d..8671dc83e43f77a6f5501c7f61e6a040eb5aa12a 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 61d89e6a6ff5bf1ffa27210ce1a077ac0634e8c9..1055fccb86df67983dd7f4d75c072b5dae989c7a 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 0a1f5ed064de30e6baaf9d20a31d4c3b28ebc5b3..f118d05a6fb03a1268de0fdfea56b963ce3514a5 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()