An Efficient Method for Probabilistic Knowledge Integration

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dc.contributor.authorZhang, Shenyong
dc.contributor.authorPeng, Yun
dc.contributor.authorWang, Xiaopu
dc.date.accessioned2018-11-28T19:25:26Z
dc.date.available2018-11-28T19:25:26Z
dc.date.issued2008-11-03
dc.descriptionProceedings of The 20th IEEE International Conference on Tools with Artificial Intelligenceen_US
dc.description.abstractThis paper presents an efficient method, SMOOTH, for modifying a joint probability distribution to satisfy a set of inconsistent constraints. It extends the well-known “iterative proportional fitting procedure” (IPFP), which only works with consistent constraints. Comparing with existing methods, SMOOTH is computationally more efficient and insensitive to data. Moreover, SMOOTH can be easily integrated with Bayesian networks for Bayes reasoning with inconsistent constraints.en_US
dc.description.sponsorshipThis work was supported in part by NIST award 60NANB6D6206 and the China Scholarship Council (CSC).en_US
dc.description.urihttps://ieeexplore.ieee.org/document/4669772en_US
dc.format.extent4 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M22F7JV9K
dc.identifier.citationShenyong Zhang, Yun Peng, and Xiaopu Wang, An Efficient Method for Probabilistic Knowledge Integration, Proceedings of The 20th IEEE International Conference on Tools with Artificial Intelligence, 2008, https://ieeexplore.ieee.org/document/4669772en_US
dc.identifier.uri10.1109/ICTAI.2008.57
dc.identifier.urihttp://hdl.handle.net/11603/12117
dc.language.isoen_USen_US
dc.publisherIEEEen_US
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.relation.ispartofUMBC Student Collection
dc.relation.ispartof© 2008 IEEE
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.rights© 2008 IEEE
dc.subjectProbabilisticen_US
dc.subjectKnowledge Integrationen_US
dc.subjectSMOOTHen_US
dc.subjectBayesian networken_US
dc.subjectstatistical distributionsen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.subjectiterative proportional fitting procedure (IPFP)en_US
dc.titleAn Efficient Method for Probabilistic Knowledge Integrationen_US
dc.typeTexten_US

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