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Approximation Methods for Efficient Learning of Bayesian Networks
Carsten Riggelsen
出版IOS Press, 2008
主題Computers / Data Science / Neural NetworksMathematics / Probability & Statistics / General
ISBN15860382149781586038212
URLhttp://books.google.com.hk/books?id=37kO0psfWkcC&hl=&source=gbs_api
EBookSAMPLE
註釋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.