Approximation Methods for Efficient Learning of Bayesian Networks.

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Bibliographic Details
Author / Creator:	Riggelsen, C.
Imprint:	Amsterdam : IOS Press, 2008.
Description:	1 online resource (148 pages).
Language:	English
Series:	Frontiers in artificial intelligence and applications Frontiers in artificial intelligence and applications.
Subject:	Bayesian statistical decision theory. Machine learning. Neural networks (Computer science) Bayesian statistical decision theory. Machine learning. Neural networks (Computer science)
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12450391

Hidden Bibliographic Details
Varying Form of Title:	Frontiers in Artificial Intelligence and Applications
ISBN:	1586038214 9781586038212
Notes:	English. Print version record.
Summary:	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. The 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 t.

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