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

Cannon, Stephen J.

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

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
dc.date.available	2008-07-24T20:25:58Z
dc.date.issued	2008-07-24T20:25:58Z
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.identifier.uri	https://hdl.handle.net/1920/3181
dc.language.iso	en_US
dc.subject	Bayesian
dc.subject	Network
dc.subject	PDBN
dc.subject	BN Generator
dc.subject	SPI
dc.subject	Boyen-Koller
dc.title	Analysis of the Relationship between Partially Dynamic Bayesian Network Architecture and Inference Algorithm Effectiveness
dc.type	Thesis
thesis.degree.discipline	Systems Engineering
thesis.degree.grantor	George Mason University
thesis.degree.level	Master's
thesis.degree.name	Master of Science in Systems Engineering