Exploiting the probability of observation for efficient Bayesian network inference

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dc.contributor.authorMousumi, Fouzia Ashraf
dc.contributor.supervisorGrant, Kevin
dc.contributor.supervisorWismath, Stephen
dc.date.accessioned2014-06-20T19:35:41Z
dc.date.available2014-06-20T19:35:41Z
dc.date.issued2013
dc.degree.levelMastersen_US
dc.degree.levelMasters
dc.descriptionxi, 88 leaves : ill. ; 29 cmen_US
dc.description.abstractIt is well-known that the observation of a variable in a Bayesian network can affect the effective connectivity of the network, which in turn affects the efficiency of inference. Unfortunately, the observed variables may not be known until runtime, which limits the amount of compile-time optimization that can be done in this regard. This thesis considers how to improve inference when users know the likelihood of a variable being observed. It demonstrates how these probabilities of observation can be exploited to improve existing heuristics for choosing elimination orderings for inference. Empirical tests over a set of benchmark networks using the Variable Elimination algorithm show reductions of up to 50% and 70% in multiplications and summations, as well as runtime reductions of up to 55%. Similarly, tests using the Elimination Tree algorithm show reductions by as much as 64%, 55%, and 50% in recursive calls, total cache size, and runtime, respectively.en_US
dc.identifier.urihttps://hdl.handle.net/10133/3457
dc.language.isoen_CAen_US
dc.proquestyesNoen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Scienceen_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectBayesian statistical decision theory -- Data processingen_US
dc.subjectEliminationen_US
dc.subjectDecision making -- Mathematical modelsen_US
dc.subjectDissertations, Academicen_US
dc.titleExploiting the probability of observation for efficient Bayesian network inferenceen_US
dc.typeThesisen_US
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