Bayesian Artificial Intelligence

AvKevin B. Korb,Ann E. Nicholson

Häftad, Engelska, 2023

Del i serien Chapman & Hall/CRC Computer Science & Data Analysis

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Beskrivning

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second Edition New chapter on Bayesian network classifiersNew section on object-oriented Bayesian networksNew section that addresses foundational problems with causal discovery and Markov blanket discoveryNew section that covers methods of evaluating causal discovery programsDiscussions of many common modeling errorsNew applications and case studiesMore coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems.Web ResourceThe book’s website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.

Produktinformation

Utgivningsdatum:2023-01-21
Mått:156 x 234 x 36 mm
Vikt:740 g
Format:Häftad
Språk:Engelska
Serie:Chapman & Hall/CRC Computer Science & Data Analysis
Antal sidor:492
Upplaga:2
Förlag:Taylor & Francis Ltd
ISBN:9781032477657

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Mer om författaren

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

Recensioner i media

… 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), 79Praise 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)