"A nice feature of the book is the extensive survey of the available software, much of it downloadable for free on the web. [This book] provides a very solid introduction to BNs for those statisticians who may have heard about BNs but are unfamiliar with their basics. The many examples clearly illustrate the topics, and there are many hints at the broader applications." - Technometrics, Feb. 2005, Vol. 47, No. 1 "This book certainly deserves to be in the library of any institution where undergraduate or graduate courses in computer science are taught, and would also be an excellent resource for anyone who wants to learn more about this cutting-edge area of computing. Summing Up: Essential." - Choice, June 2004, Vol. 41, No. 10" 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)
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
--This text refers to an alternate Hardcover