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  <div class="section" id="module-pebl.prior">
<span id="prior-prior-models"></span><h1><tt class="xref py py-mod docutils literal"><span class="pre">prior</span></tt> &#8211; Prior models<a class="headerlink" href="#module-pebl.prior" title="Permalink to this headline"></a></h1>
<p>This module provides classes for representing and working with prior models.</p>
<dl class="class">
<dt id="pebl.prior.Prior">
<em class="property">class </em><tt class="descclassname">pebl.prior.</tt><tt class="descname">Prior</tt><big>(</big><em>num_nodes</em>, <em>energy_matrix=None</em>, <em>required_edges=</em><span class="optional">[</span><span class="optional">]</span>, <em>prohibited_edges=</em><span class="optional">[</span><span class="optional">]</span>, <em>constraints=</em><span class="optional">[</span><span class="optional">]</span>, <em>weight=1.0</em><big>)</big><a class="headerlink" href="#pebl.prior.Prior" title="Permalink to this definition"></a></dt>
<dd><p>Class for representing prior model.</p>
<blockquote>
<div><dl class="docutils">
<dt>Priors have two aspects: </dt>
<dd><ul class="first last simple">
<li>soft priors: weights for each possible edge.</li>
<li>hard priors: constraints that MUST be met.</li>
</ul>
</dd>
</dl>
<p>Soft priors are specified by an energy matrix, a weight matrix taking
values over [0,1.0].</p>
<dl class="docutils">
<dt>Hard priors are specified as:</dt>
<dd><ul class="first last">
<li><p class="first">required_edges: a list of edge-tuples that must be rpesent</p>
</li>
<li><p class="first">prohibited_edges: a list of edge-tuples that must not be present</p>
</li>
<li><dl class="first docutils">
<dt>constraints: a list of functions that take adjacency matrix as input</dt>
<dd><p class="first last">and return true if constraint met and false otherwise.</p>
</dd>
</dl>
</li>
</ul>
</dd>
</dl>
</div></blockquote>
<p>For more information about calculating prior probabilities via energy
matrices, consult:</p>
<blockquote>
<div><ol class="arabic simple">
<li>Imoto, S. and Higuchi, T. and Goto, T. and Tashiro, K. and Kuhara, S.
and Miyano, S. Combining microarrays and biological knowledge for
estimating gene networks via Bayesian networks. Proc IEEE Comput Soc
Bioinform Conf. 2003, p.104-113.</li>
</ol>
</div></blockquote>
<dl class="method">
<dt id="pebl.prior.Prior.loglikelihood">
<tt class="descname">loglikelihood</tt><big>(</big><em>net</em><big>)</big><a class="headerlink" href="#pebl.prior.Prior.loglikelihood" title="Permalink to this definition"></a></dt>
<dd><p>Returns the log likelihood of the given network.</p>
<p>Similar to the loglikelihood method of a Conditional Probability
Distribution.</p>
</dd></dl>

</dd></dl>

<dl class="class">
<dt id="pebl.prior.UniformPrior">
<em class="property">class </em><tt class="descclassname">pebl.prior.</tt><tt class="descname">UniformPrior</tt><big>(</big><em>num_nodes</em>, <em>weight=1.0</em><big>)</big><a class="headerlink" href="#pebl.prior.UniformPrior" title="Permalink to this definition"></a></dt>
<dd><p>A uniform prior &#8211; that is, every edge is equally likely.</p>
</dd></dl>

<dl class="class">
<dt id="pebl.prior.NullPrior">
<em class="property">class </em><tt class="descclassname">pebl.prior.</tt><tt class="descname">NullPrior</tt><big>(</big><em>*args</em>, <em>**kwargs</em><big>)</big><a class="headerlink" href="#pebl.prior.NullPrior" title="Permalink to this definition"></a></dt>
<dd><p>A null prior which returns 0.0 for the loglikelihood.</p>
<p>The name for this object is a bit confusing because the UniformPrior is
often considered the null prior in that it doesn&#8217;t favor any edge more than
another. It still favors smaller networks and takes time to calculate the
loglikelihood. This class provides an implementation that simply returns
0.0 for the loglikelihood. It&#8217;s a null prior in the sense that the
resulting scores are the same as if you hadn&#8217;t used a prior at all.</p>
</dd></dl>

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