Studies in Fuzziness and Soft Computing

Gámez, José A.; Moral, Serafin; Salmerón Cerdan, Antonio

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

Details

Published by: Springer

Publication Date: 2004-02-23

Format: Hardcover

ISBN-13: 9783540208761

DOI: 10.1007/978-3-540-39879-0

Dimensions: 234cm x156cm

Pages: 328