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<section id="user-guide">
<h1>USER GUIDE<a class="headerlink" href="#user-guide" title="Link to this heading">¶</a></h1>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
<p>BayesValidRox provides functionalities for describing uncertain parameters, building surrogate models based on model outputs and evaluating them with Bayesian validation methods.</p>
<p>This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on.
It can be installed with pip, best practice is to do so inside a virtual environment.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>-m<span class="w"> </span>venv<span class="w"> </span>bayes_env
<span class="nb">cd</span><span class="w"> </span>bayes_env
<span class="nb">source</span><span class="w"> </span>bin/activate
</pre></div>
</div>
<p>Here replace <code class="docutils literal notranslate"><span class="pre">bayes_env</span></code> with your preferred name.
Then install the latest release of BayesValidRox inside the venv.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>bayesvalidrox
</pre></div>
</div>
<p>The current master can be installed by cloning the repository.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox.git
<span class="nb">cd</span><span class="w"> </span>bayesvalidrox
pip<span class="w"> </span>install<span class="w"> </span>.
</pre></div>
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<section id="overview">
<h3>Overview<a class="headerlink" href="#overview" title="Link to this heading">¶</a></h3>
<p>This package is split into multiple aspects corresponding to its folder structure.</p>
<a class="reference internal image-reference" href="_images/folder_structure.png"><img alt="Folder structure of **bayesvalidrox**" src="_images/folder_structure.png" style="width: 600px;" /></a>
<p>The folder <code class="docutils literal notranslate"><span class="pre">surrogate_models</span></code> contains all the functions and classes that are necessary in order to create and train the surrogate model.
This includes</p>
<ul class="simple">
<li><p>defining the input marginals</p></li>
<li><p>setting properties of the sampling in an experimental design</p></li>
<li><p>choosing the surrogate model and its properties</p></li>
<li><p>training the surrogate model on model evaluations in a straightforward manner or iteratively with active learning</p></li>
</ul>
<p>The computational model is linked via a <code class="docutils literal notranslate"><span class="pre">pylink</span></code> interface.</p>
<p>Multiple post-processing options are available, including the calculation of Sobol’ indices, checking the accuracy of the surrogate model and visualizations of the moments of the surrogate.</p>
<p>Bayesian inference can be performed with rejection sampling or MCMC, while taking into account the estimated uncertainty of the data that the (surrogate) model is compared to.
If multiple (surrogate) models are given, they can be compared against each other with pairwise Bayes Factors, model weights or a justifiability analysis.</p>
<p>The next pages lead through the topics given in BayesValidRox and describe the available classes and give brief examples for their use.</p>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="input_description.html">Priors, input space and experimental design</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_description.html">Models</a></li>
<li class="toctree-l1"><a class="reference internal" href="surrogate_description.html">Training surrogate models</a></li>
<li class="toctree-l1"><a class="reference internal" href="al_description.html">Active learning: iteratively expanding the training set</a></li>
<li class="toctree-l1"><a class="reference internal" href="post_description.html">Postprocessing</a></li>
<li class="toctree-l1"><a class="reference internal" href="bayes_description.html">Bayesian inference</a></li>
<li class="toctree-l1"><a class="reference internal" href="bmc_description.html">Bayesian multi-model comparison</a></li>
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