File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Bayesian MAP model selection of chain event graphs

TitleBayesian MAP model selection of chain event graphs
Authors
KeywordsBayesian model selection
Chain event graphs
Dirichlet distribution
Issue Date2011
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva
Citation
Journal of Multivariate Analysis, 2011, v. 102 n. 7, p. 1152-1165 How to Cite?
AbstractChain event graphs are graphical models that while retaining most of the structural advantages of Bayesian networks for model interrogation, propagation and learning, more naturally encode asymmetric state spaces and the order in which events happen than Bayesian networks do. In addition, the class of models that can be represented by chain event graphs for a finite set of discrete variables is a strict superset of the class that can be described by Bayesian networks. In this paper we demonstrate how with complete sampling, conjugate closed form model selection based on product Dirichlet priors is possible, and prove that suitable homogeneity assumptions characterise the product Dirichlet prior on this class of models. We demonstrate our techniques using two educational examples. © 2011 Elsevier Inc.
Persistent Identifierhttp://hdl.handle.net/10722/164797
ISSN
2015 Impact Factor: 0.857
2015 SCImago Journal Rankings: 1.458
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFreeman, Gen_US
dc.contributor.authorSmith, JQen_US
dc.date.accessioned2012-09-20T08:09:41Z-
dc.date.available2012-09-20T08:09:41Z-
dc.date.issued2011en_US
dc.identifier.citationJournal of Multivariate Analysis, 2011, v. 102 n. 7, p. 1152-1165en_US
dc.identifier.issn0047-259X-
dc.identifier.urihttp://hdl.handle.net/10722/164797-
dc.description.abstractChain event graphs are graphical models that while retaining most of the structural advantages of Bayesian networks for model interrogation, propagation and learning, more naturally encode asymmetric state spaces and the order in which events happen than Bayesian networks do. In addition, the class of models that can be represented by chain event graphs for a finite set of discrete variables is a strict superset of the class that can be described by Bayesian networks. In this paper we demonstrate how with complete sampling, conjugate closed form model selection based on product Dirichlet priors is possible, and prove that suitable homogeneity assumptions characterise the product Dirichlet prior on this class of models. We demonstrate our techniques using two educational examples. © 2011 Elsevier Inc.-
dc.languageengen_US
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva-
dc.relation.ispartofJournal of Multivariate Analysisen_US
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Multivariate Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Multivariate Analysis, 2011, v. 102 n. 7, p. 1152-1165. DOI: 10.1016/j.jmva.2011.03.008-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBayesian model selection-
dc.subjectChain event graphs-
dc.subjectDirichlet distribution-
dc.titleBayesian MAP model selection of chain event graphsen_US
dc.typeArticleen_US
dc.identifier.emailFreeman, G: gfreeman@hku.hken_US
dc.description.naturepreprint-
dc.identifier.doi10.1016/j.jmva.2011.03.008-
dc.identifier.scopuseid_2-s2.0-79956295822-
dc.identifier.hkuros209016en_US
dc.identifier.volume102en_US
dc.identifier.issue7-
dc.identifier.spage1152en_US
dc.identifier.epage1165en_US
dc.identifier.isiWOS:000291520300005-
dc.publisher.placeUnited States-
dc.identifier.citeulike9159438-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats