Bayesian Artificial Intelligence

by Kevin B. Korb, Ann E. Nicholson

Overview

The second edition of this bestseller provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. This edition contains a new chapter on Bayesian network classifiers and a new section on object-oriented Bayesian networks, along with new applications and case studies. It includes a new section that addresses foundational problems with causal discovery and Markov blanket discovery and a new section that covers methods of evaluating causal discovery programs. The book also offers more coverage on the uses of causal interventions to understand and reason with causal Bayesian networks. Supplemental materials are available on the book's website.

Product Details

ISBN-13:	9781040055274
Publisher:	CRC Press
Publication date:	12/16/2010
Series:	Chapman & Hall/CRC Computer Science & Data Analysis
Sold by:	Barnes & Noble
Format:	eBook
Pages:	491
File size:	19 MB
Note:	This product may take a few minutes to download.

About the Author

Kevin B. Korb is a Reader in the Clayton School of Information Technology at Monash University in Australia. He earned his Ph.D. from Indiana University. His research encompasses causal discovery, probabilistic causality, evaluation theory, informal logic and argumentation, artificial evolution, and philosophy of artificial intelligence.

Ann E. Nicholson an Associate Professor in the Clayton School of Information Technology at Monash University in Australia. She earned her Ph.D. from the University of Oxford. Her research interests include artificial intelligence, probabilistic reasoning, Bayesian networks, knowledge engineering, plan recognition, user modeling, evolutionary ethics, and data mining

Table of Contents

Probabilistic Reasoning. Learning Causal Models. Knowledge Engineering. Appendices. References. Index.

Editorial Reviews

… useful insights on Bayesian reasoning. … There are extensive examples of applications and case studies. … The exposition is clear, with many comments that help set the context for the material that is covered. The reader gets a strong sense that Bayesian networks are a work in progress.
—John H. Maindonald, International Statistical Review (2011), 79

Praise for the First Edition:
… this excellent book would also serve well for final year undergraduate courses in mathematics or statistics and is a solid first reference text for researchers wanting to implement Bayesian belief network (BBN) solutions for practical problems. … beautifully presented, nicely written, and made accessible. Mathematical ideas, some quite deep, are presented within the flow but do not get in the way. This has the advantage that students can see and interpret the mathematics in the practical context, whereas practitioners can acquire, to personal taste, the mathematical seasoning. If you are interested in applying BBN methods to real-life problems, this book is a good place to start…
—Journal of the Royal Statistical Society, Series A, Vol. 157(3)

From the Publisher