http://siteframe.org/bayesnets2 <div align="center"><strong>Bayes Nets</strong></div> <div align="center">Links to other web pages: <a href="http://www.bayesnets.com/BayesNetsConcepts.html">Basic Concepts</a> | <a href="http://www.bayesnets.com/CausalityReferences.htm">Causality</a> | <a href="http://www.bayesnets.com/Classifiers.htm">Classifiers</a> | <a href="http://www.bayesnets.com/Inference.htm">Inference</a> | <a href="http://www.bayesnets.com/Learning.htm">Learning</a> | <a href="http://www.bayesnets.com/ReadingListsforBayesNets.htm">Reading List</a> | <a href="http://www.bayesnets.com/relatedTopics.htm">Related Topics</a></div> <div align="center">&nbsp;</div> <div align="center">Links in this web page: <a href="http://www.bayesnets.com/#Conferences">Conferences</a> | <a href="http://www.bayesnets.com/#Database">Database</a> | <a href="http://www.bayesnets.com/#Groups">Groups</a> | <a href="http://www.bayesnets.com/#Introduction">Introduction</a> | <a href="http://www.bayesnets.com/#Journals">Journals</a> | <a href="http://www.bayesnets.com/#References">References</a> | <a href="http://www.bayesnets.com/#Researchers">Researchers</a> | <a href="http://www.bayesnets.com/#Resources">Resources</a> | <a href="http://www.bayesnets.com/#Software">Software</a></div> <div align="center">&nbsp;</div> <div><strong><a name="Introduction">Introduction</a></strong></div> <div style="line-height: 200%">Bayesian networks [79] are graphical representation for probabilistic relationships among a set of random variables. Given a finite set &nbsp;of discrete random variables where each variable &nbsp;may take values from a finite set, denoted by . A Bayesian network is an annotated directed acyclic graph (DAG) G that encodes a joint probability distribution over. The nodes of the graph correspond to the random variables. The links of the graph correspond to the direct influence from one variable to the other. If there is a directed link from variable &nbsp;to variable , variable &nbsp;will be a parent of variable . Each node is annotated with a conditional probability distribution (CPD) that represents, where &nbsp;denotes the parents of &nbsp;in. The pair (, CPD) encodes the joint distribution. A unique joint probability distribution over &nbsp;from &nbsp;is factorized as:</div> <div style="line-height: 200%">Figure <span>2</span> is an example of a Bayesian network from Cooper&rsquo;s paper [16], which is hypothetically about the medical domain with 5 variables. The corresponding CPDs are in Table <span>1</span>.</div> en-US &copy;2005-7 Glen Campbell Sun, 12 Oct 2008 17:20:02 PDT Siteframe 5.0.5 webmaster@siteframe.org http://blogs.law.harvard.edu/tech/rss 30