Approximation of Marginal Probabilities while Learning Bayesian Networks

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" Approximation of Marginal Probabilities while Learning Bayesian Networks "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	151677
Doc. No	:	ET23469
Main Entry	:	Joseph E. Cannon
Title Proper	:	Approximation of Marginal Probabilities while Learning Bayesian Networks
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	Computation of marginal probabilities in Bayesian Belief Networks is central to manyprobabilistic reasoning systems and automatic decision making systems. The process ofbelief updating in Bayesian Belief Networks (BBN) is a well-known computationallyhard problem that has recently been approximated by several deterministic algorithms......-....,....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL46709.pdf
Title and statement of responsibility and	:	Approximation of Marginal Probabilities while Learning Bayesian Networks [Thesis]