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Analysis of the Relationship between Partially Dynamic Bayesian Network Architecture and Inference Algorithm Effectiveness

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dc.contributor.author Cannon, Stephen J.
dc.creator Cannon, Stephen J.
dc.date 2007-12-04
dc.date.accessioned 2008-07-24T20:25:58Z
dc.date.available NO_RESTRICTION en
dc.date.available 2008-07-24T20:25:58Z
dc.date.issued 2008-07-24T20:25:58Z
dc.identifier.uri https://hdl.handle.net/1920/3181
dc.description.abstract This thesis examines the relationship between the architecture of partially dynamic Bayesian networks and the effectiveness of various inference algorithms using these Bayesian networks. The algorithms studied were the symbolic probabilistic inference algorithm, the particle filter inference algorithm, and Boyen-Koller inference algorithm. The purpose of this research is to provide empirical support for theoretical models of the speed and accuracy of each of these inference algorithms as well as to develop statistical models that utilize computationally and conceptually simple factors. The author shows that the empirical results for the speed of inference of each inference algorithm generally agrees with the theoretical complexity models of each algorithm. The author also developed empirical models that predict the variance of speed of each of the inference algorithms explored in this research.
dc.language.iso en_US en
dc.subject Bayesian en_US
dc.subject Network en_US
dc.subject PDBN en_US
dc.subject BN Generator en_US
dc.subject SPI en_US
dc.subject Boyen-Koller en_US
dc.title Analysis of the Relationship between Partially Dynamic Bayesian Network Architecture and Inference Algorithm Effectiveness en
dc.type Thesis en
thesis.degree.name Master of Science in Systems Engineering en
thesis.degree.level Master's en
thesis.degree.discipline Systems Engineering en
thesis.degree.grantor George Mason University en


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