Approximation methods for efficient learning of Bayesian networks
著者
書誌事項
Approximation methods for efficient learning of Bayesian networks
（Frontiers in artificial intelligence and applications, v. 168 . Dissertations in artificial intelligence）
IOS Press, c2008
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注記
Includes bibliographical references (p. [133]-137)
内容説明・目次
内容説明
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 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(in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.
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