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(in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields.     Some of the areas we publish in:    -Biomedicine   -Oncology   -Artificial intelligence   -Databases and information systems   -Maritime engineering   -Nanotechnology   -Geoengineering   -All aspects of physics   -E-governance   -E-commerce   -The knowledge economy   -Urban studies   -Arms control   -Understanding and responding to terrorism   -Medical informatics   -Computer Sciences

Approximation Methods for Efficient Learning of Bayesian Networks PDF

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