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dc.contributor.supervisor Grant, Kevin
dc.contributor.supervisor Wismath, Stephen
dc.contributor.author Mousumi, Fouzia Ashraf
dc.date.accessioned 2014-06-20T19:35:41Z
dc.date.available 2014-06-20T19:35:41Z
dc.date.issued 2013
dc.identifier.uri https://hdl.handle.net/10133/3457
dc.description xi, 88 leaves : ill. ; 29 cm en_US
dc.description.abstract It 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.language.iso en_CA en_US
dc.publisher Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science en_US
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science) en_US
dc.subject Bayesian statistical decision theory -- Data processing en_US
dc.subject Elimination en_US
dc.subject Decision making -- Mathematical models en_US
dc.subject Dissertations, Academic en_US
dc.title Exploiting the probability of observation for efficient Bayesian network inference en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Mathematics and Computer Science en_US
dc.degree.level Masters en_US
dc.degree.level Masters
dc.proquestyes No en_US


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