
Bayesian Artificial Intelligence
CRC Press
2nd Edition
Published on 21. January 2023
Book
Paperback/Softback
492 pages
978-1-032-47765-7 (ISBN)
Description
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 classifiers
New section on object-oriented Bayesian networks
New section that addresses foundational problems with causal discovery and Markov blanket discovery
New section that covers methods of evaluating causal discovery programs
Discussions of many common modeling errors
New applications and case studies
More 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 Resource
The 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.
New to the Second Edition
New chapter on Bayesian network classifiers
New section on object-oriented Bayesian networks
New section that addresses foundational problems with causal discovery and Markov blanket discovery
New section that covers methods of evaluating causal discovery programs
Discussions of many common modeling errors
New applications and case studies
More 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 Resource
The 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.
Reviews / Votes
... 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)
More details
Series
Edition
2nd edition
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Professional Practice & Development
Product notice
Paperback (trade)
Unsewn / adhesive bound
Illustrations
159 s/w Abbildungen
159 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 155 mm
Thickness: 36 mm
Weight
740 gr
ISBN-13
978-1-032-47765-7 (9781032477657)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Kevin B. Korb | Ann E. Nicholson
Bayesian Artificial Intelligence
E-Book
12/2010
2nd Edition
CRC Press
€64.49
Available for download

Kevin B. Korb | Ann E. Nicholson
Bayesian Artificial Intelligence
Book
12/2010
2nd Edition
CRC Press
€207.30
Shipment within 15-20 days

Kevin B. Korb | Ann E. Nicholson
Bayesian Artificial Intelligence
E-Book
12/2010
2nd Edition
CRC Press
€64.49
Available for download
Persons
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
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
Author
Monash University, Clayton, Victoria, Australia
Monash University, Clayton, Victoria, Australia
Content
Probabilistic Reasoning. Learning Causal Models. Knowledge Engineering. Appendices. References. Index.