Modeling and Reasoning with Bayesian Networks

Adnan Darwiche

ISBN 10: 0521884381 / ISBN 13: 9780521884389

Published by Cambridge University Press, 2009

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Seller Inventory # 9780521884389

Bibliographic Details

• Title: Modeling and Reasoning with Bayesian Networks
• Publisher: Cambridge University Press
• Publication Date: 2009
• Language: English
• ISBN 10: 0521884381
• ISBN 13: 9780521884389
• Binding: Buch
• Condition: Neu
• Dimensions: 260W x 183H x 35D millimeters 260 millimeters width by 183 millimeters height by 35 millimeters depth
• Weight: 1239 grams

About this title

Synopsis

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

About the Author

Adnan Darwiche is a Professor in the Department of Computer Science at the University of California, Los Angeles.