Approximation Methods for Efficient Learning of Bayesian Networks

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Author
Riggelsen, C.
Pub. date
January 2008
Pages
148
Binding
softcover
Volume
168 of Frontiers in Artificial Intelligence and Applications
ISBN print
978-1-58603-821-2
ISBN online
978-1-60750-298-2
Subject
Artificial Intelligence, Computer Science
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This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. Topics discussed are; basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way material previously published by the author, with unpublished work.