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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>
</div>
<div class="section" id="concepts">
<h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this headline"></a></h2>
<p>All Pebl analysis include <a class="reference internal" href="#term-data"><span class="xref std std-term">data</span></a>, a <a class="reference internal" href="#term-learner"><span class="xref std std-term">learner</span></a> and a <a class="reference internal" href="#term-result"><span class="xref std std-term">result</span></a>.  They may also
include <a class="reference internal" href="#term-prior-models"><span class="xref std std-term">prior models</span></a> and <a class="reference internal" href="#term-task-controllers"><span class="xref std std-term">task controllers</span></a>.</p>
<dl class="glossary docutils">
<dt id="term-data">Data</dt>
<dd>This is the set of observations that is used to score a given network.
The data can include missing values and hidden/unobserved variables and
observations can be marked as being the result of specific
interventions. Data can be read from a file or created programatically.</dd>
<dt id="term-learner">Learner</dt>
<dd>A learner implements a specific learning algorithm. It is given some
data, prior model and a stopping criteria and returns a <a class="reference internal" href="#term-result"><span class="xref std std-term">result</span></a>
object.</dd>
<dt id="term-result">Result</dt>
<dd>A result object contains a list of the top-scoring networks found
during a learner run and some statistics about the analysis. Results
from different learning runs with the same data can be merged and
visualized in various formats.</dd>
<dt id="term-prior-models">Prior Models</dt>
<dd>A key strength of Bayesian analysis is the ability to integrate
knowledge with observations. A Pebl prior model specifies the prior
belief about the set of possible networks and can include hard and soft
constraints.</dd>
<dt id="term-task-controllers">Task Controllers</dt>
<dd>Pebl uses task controllers to run analyses in parallel.  Users can
utilize multiple CPU cores or computational clusters without managing
any of the details related to parallel programming.</dd>
</dl>
</div>
</div>


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  <h3><a href="index.html">Table Of Contents</a></h3>
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<li><a class="reference internal" href="#">Pebl Introduction</a><ul>
<li><a class="reference internal" href="#availability">Availability</a></li>
<li><a class="reference internal" href="#concepts">Concepts</a></li>
</ul>
</li>
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      <li>Previous: <a href="index.html" title="previous chapter">Pebl Documentation!</a></li>
      <li>Next: <a href="install.html" title="next chapter">Installation</a></li>
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