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<div class="section" id="module-pebl.posterior">
<span id="posterior-posterior-distribution"></span><h1><tt class="xref py py-mod docutils literal"><span class="pre">posterior</span></tt> – Posterior distribution<a class="headerlink" href="#module-pebl.posterior" title="Permalink to this headline">¶</a></h1>
<p>This module provides a class for representing and working with posterior distribution.</p>
<dl class="class">
<dt id="pebl.posterior.Posterior">
<em class="property">class </em><tt class="descclassname">pebl.posterior.</tt><tt class="descname">Posterior</tt><big>(</big><em>nodes</em>, <em>adjacency_matrices</em>, <em>scores</em>, <em>sortedscores=False</em><big>)</big><a class="headerlink" href="#pebl.posterior.Posterior" title="Permalink to this definition">¶</a></dt>
<dd><p>Class for representing posterior distribution.</p>
<p>Except for trivial cases, we can only have an estimated posterior
distribution. It is usually constructed as a list of the top N networks
found during a search of the space of networks.</p>
<p>The pebl posterior object supports a list-like interface. So, given a
posterior object post, one can do the following:</p>
<blockquote>
<div><ul class="simple">
<li>Access the top-scoring network: post[0]</li>
<li>Access the top 10 networks as a new posterior object: post[0:10]</li>
<li>Calculate entropy of distribution: post.entropy</li>
<li>Iterate through networks: for net in post: print net.score</li>
</ul>
</div></blockquote>
<p>Note: a posterior object is immutable. That is, you cannot add and remove
networks once it is created. See result.Result for a mutable container for
networks.</p>
<p>Creates a posterior object.</p>
<p>adjacency_matrices and scores can be lists or numpy arrays.
If sorted is True, adjacency_matrices and scores should be sorted in
descending order of score.</p>
<dl class="method">
<dt id="pebl.posterior.Posterior.consensus_network">
<tt class="descname">consensus_network</tt><big>(</big><em>threshold=0.29999999999999999</em><big>)</big><a class="headerlink" href="#pebl.posterior.Posterior.consensus_network" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a consensus network with the given threshold.</p>
</dd></dl>
<dl class="attribute">
<dt id="pebl.posterior.Posterior.entropy">
<tt class="descname">entropy</tt><a class="headerlink" href="#pebl.posterior.Posterior.entropy" title="Permalink to this definition">¶</a></dt>
<dd><p>The information entropy of the posterior distribution.</p>
</dd></dl>
</dd></dl>
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