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<div class="section" id="pebl-introduction">
<span id="intro"></span><h1>Pebl Introduction<a class="headerlink" href="#pebl-introduction" title="Permalink to this headline">¶</a></h1>
<p>Pebl is a python library and command line application for learning the
structure of a Bayesian network given prior knowledge and observations. Pebl
includes the following features:</p>
<blockquote>
<div><ul class="simple">
<li>Can learn with observational and interventional data</li>
<li>Handles missing values and hidden variables using exact and heuristic
methods</li>
<li>Provides several learning algorithms; makes creating new ones simple</li>
<li>Has facilities for transparent parallel execution</li>
<li>Calculates edge marginals and consensus networks</li>
<li>Presents results in a variety of formats</li>
</ul>
</div></blockquote>
<div class="section" id="availability">
<h2>Availability<a class="headerlink" href="#availability" title="Permalink to this headline">¶</a></h2>
<p>Pebl is licensed under a permissive <a class="reference external" href="http://pebl-project.googlecode.com/svn/trunk/LICENSE.txt">MIT-style license</a> and can be
downloaded from its <a class="reference external" href="http://pebl-project.googlecode.com/">Google code site</a>
or from the <a class="reference external" href="http://pypi.python.org/pypi/pebl">Python Package Index</a>.</p>
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<div class="section" id="concepts">
<h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this headline">¶</a></h2>
<p>All Pebl analysis include <em class="xref std std-term">data</em>, a <em class="xref std std-term">learner</em> and a <em class="xref std std-term">result</em>. They may also
include <em class="xref std std-term">prior models</em> and <em class="xref std std-term">task controllers</em>.</p>
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