Integrating Probability Constraints into Bayesian Nets

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dc.contributor.authorPeng, Yun
dc.contributor.authorZhang, Shenyong
dc.date.accessioned2018-11-19T20:55:00Z
dc.date.available2018-11-19T20:55:00Z
dc.date.issued2010-11-30
dc.descriptionProceeding of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligenceen
dc.description.abstractThis paper presents a formal convergence proof for EIPFP, an algorithm that integrates low dimensional probabilistic constraints into a Bayesian network (BN) based on the mathematical procedure IPFP. It also extends E-IPFP to deal with constraints that are inconsistent with each other or with the BN structure.en
dc.description.sponsorshipThis work was supported in part by NIST award 60NANB6- D6206 and the China Scholarship Council (CSC).en
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/528/Integrating-Probability-Constraints-into-Bayesian-Netsen
dc.format.extent2 pagesen
dc.genrepreprints
dc.genreconference papers and proceedingsen
dc.identifierdoi:10.13016/M2Z60C59J
dc.identifier.citationYun Peng and Shenyong Zhang, Integrating Probability Constraints into Bayesian Nets, Proceeding of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence, 2010, https://ebiquity.umbc.edu/paper/html/id/528/Integrating-Probability-Constraints-into-Bayesian-Netsen
dc.identifier.urihttp://hdl.handle.net/11603/12065
dc.language.isoenen
dc.publisherIOS Pressen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectIntegratingen
dc.subjectProbabilityen
dc.subjectConstraintsen
dc.subjectBayesian Netsen
dc.subjectUMBC Ebiquity Research Groupen
dc.titleIntegrating Probability Constraints into Bayesian Netsen
dc.typeTexten

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