{"id":21,"date":"2019-03-15T15:13:13","date_gmt":"2019-03-15T15:13:13","guid":{"rendered":"http:\/\/groups.cs.umass.edu\/kdl\/?page_id=21"},"modified":"2019-03-15T15:13:13","modified_gmt":"2019-03-15T15:13:13","slug":"powerbayes","status":"publish","type":"page","link":"https:\/\/groups.cs.umass.edu\/kdl\/powerbayes\/","title":{"rendered":"PowerBayes"},"content":{"rendered":"<div class=\"page-content\">\n<div class=\"wrapper\">\n<article class=\"page\">PowerBayes 1.0 is a package for structure learning of Bayesian networks. It contains implementations of many common structure-learning algorithms and new algorithms using constraint satisfaction for learning models with improved structural accuracy.<\/p>\n<p>See \u201cLearning the Structure of Bayesian Networks with Constraint Satisfaction.\u201d Fast, Andrew. Ph.D. Thesis, University of Massachusetts Amherst. (2009) for additional information on the algorithms appearing in PowerBayes.<\/p>\n<p>PowerBayes 1.0 is written in Java. The PowerBayes distribution includes:<\/p>\n<ul>\n<li>Java source code<\/li>\n<li>All necessary libraries<\/li>\n<li>README file containing instructions for running structure learning algorithms<\/li>\n<\/ul>\n<p><strong><a href=\"https:\/\/kdl.cs.umass.edu\/software_files\/power-bayes-1_0.zip\">Download PowerBayes 1.0 distribution<\/a><\/strong><\/p>\n<p><strong><a href=\"https:\/\/kdl.cs.umass.edu\/software_files\/bnr.zip\">Download Bayesian network models for use with PowerBayes<\/a><\/strong><\/p>\n<p>PowerBayes is designed and implemented by the <a href=\"https:\/\/kdl.cs.umass.edu\">Knowledge Discovery Laboratory<\/a> in the <a href=\"https:\/\/www.cics.umass.edu\/\">College of Information and Computer Sciences<\/a> at the <a href=\"https:\/\/umass.edu\">University of Massachusetts Amherst<\/a>.<\/p>\n<\/article>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>PowerBayes 1.0 is a package for structure learning of Bayesian networks. It contains implementations of many common structure-learning algorithms and new algorithms using constraint satisfaction for learning models with improved structural accuracy. See \u201cLearning the Structure of Bayesian Networks with Constraint Satisfaction.\u201d Fast, Andrew. Ph.D. Thesis, University of Massachusetts Amherst. (2009) for additional information on &hellip; <a href=\"https:\/\/groups.cs.umass.edu\/kdl\/powerbayes\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;PowerBayes&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-21","page","type-page","status-publish","hentry","hfeed"],"_links":{"self":[{"href":"https:\/\/groups.cs.umass.edu\/kdl\/wp-json\/wp\/v2\/pages\/21","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/groups.cs.umass.edu\/kdl\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/groups.cs.umass.edu\/kdl\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/kdl\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/kdl\/wp-json\/wp\/v2\/comments?post=21"}],"version-history":[{"count":1,"href":"https:\/\/groups.cs.umass.edu\/kdl\/wp-json\/wp\/v2\/pages\/21\/revisions"}],"predecessor-version":[{"id":22,"href":"https:\/\/groups.cs.umass.edu\/kdl\/wp-json\/wp\/v2\/pages\/21\/revisions\/22"}],"wp:attachment":[{"href":"https:\/\/groups.cs.umass.edu\/kdl\/wp-json\/wp\/v2\/media?parent=21"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}