Bayesian artificial intelligence

Select an Action

Place Hold(s)
Add to My Lists
Email
Print

Title:

Personal Author:

Korb, Kevin B.

Series:

Chapman & hall/crc computer science & data analysis ; 16

Edition:

2nd ed.

Publication Information:

Boca Raton, FL : CRC Press, 2010

Physical Description:

xxvii, 463 p. : ill. ; 25 cm.

ISBN:

9781439815915

Abstract:

"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 books 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"-- Provided by publisher.

"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"-- Provided by publisher.

Subject Term:

Bayesian statistical decision theory -- Data processing

Machine learning

Neural networks (Computer science)

Added Author:

Nicholson, Ann E.

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010274783	QA279.5 K67 2010	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	30000003499526	QA279.5 K67 2010	Open Access Book	Book	Searching... Unknown

On Order

Summary

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.

Author Notes

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

Reviews 1

Choice Review

Korb and Nicholson (both, Monash Univ., Australia) say in their preface that this book is aimed at advanced undergraduates in computer science who have some background in artificial intelligence, and at those who wish to engage in applied or pure research in applications of Bayesian inference in AI. They also explain how this book is different--an emphasis on causal discovery and interpretation of Bayesian networks and discussion of applications. The book has the following noteworthy features: clear and helpful introductions and summaries for each chapter, problems for each chapter for reinforcement, lists of up-to-date references, and an annotated list of relevant software showing features availability, etc. The three parts of the book, dealing respectively with fundamental background material on probabilistic reasoning, causal models, and knowledge engineering, treat a wide span of relevant material and case studies, written lucidly and carrying readers smoothly from the simple to the complex. 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. ^BSumming Up: Essential. General readers; upper-division undergraduates through professionals; two-year technical program students. R. Bharath emeritus, Northern Michigan University