Approximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications

by C. Riggelsen

Overview

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(in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

Product Details

ISBN-13:	9781586038212
Publisher:	I O S Press, Incorporated
Publication date:	01/15/2008
Series:	Frontiers in Artificial Intelligence and Applications Series , #168
Pages:	137
Product dimensions:	6.20(w) x 9.40(h) x 0.40(d)