Active Tuples-based Scheme for Bounding Posterior Beliefs

The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any available scheme that bounds the probability of evidence or posterior marginals and enhance its performance in an anytime...

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Bibliographic Details
Published inarXiv.org
Main Authors Bidyuk, Bozhena, Dechter, Rina, Rollon, Emma
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.01.2014
Subjects
Algorithms
Bayesian analysis
Computer Science - Artificial Intelligence
Upper bounds
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Summary:The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any available scheme that bounds the probability of evidence or posterior marginals and enhance its performance in an anytime manner. The scheme uses the cutset conditioning principle to tighten existing bounding schemes and to facilitate anytime behavior, utilizing a fixed number of cutset tuples. The accuracy of the bounds improves as the number of used cutset tuples increases and so does the computation time. We demonstrate empirically the value of our scheme for bounding posterior marginals and probability of evidence using a variant of the bound propagation algorithm as a plug-in scheme.
ISSN:2331-8422
DOI:10.48550/arxiv.1401.3833