Bayesian Artificial Intelligence

Bayesian Artificial Intelligence

3.89 (19 ratings by Goodreads)
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As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' more

Product details

  • Hardback | 392 pages
  • 165 x 240 x 25mm | 698.54g
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • United States
  • English
  • 106 black & white illustrations
  • 1584883871
  • 9781584883876

Review quote

"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)show more

Table of contents

Bayesian Reasoning. Introduction to Bayesian Networks. Inference in Bayesian Networks. Bayesian Network Applications. Bayesian Planning and Decision-Making. Bayesian Network Applications II. Learning Bayesian Networks I. Learning Bayesian Networks II. Causality vs. Probability. Knowledge Engineering with Bayesian Networks I. Knowledge Engineering with Bayesian Networks II. Application more

Rating details

19 ratings
3.89 out of 5 stars
5 21% (4)
4 47% (9)
3 32% (6)
2 0% (0)
1 0% (0)
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