As the power of Bayesian techniques have become more fully realized, the field of artificial intelligence (AI) 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' Web site.
Author(s): Kevin B. Korb, Ann E. Nicholson
Series: Series in computer science and data analysis
Edition: 1
Publisher: Chapman & Hall/CRC
Year: 2004
Language: English
Commentary: 62667
Pages: 391
City: Boca Raton
Book Cover......Page 1
Title......Page 4
Copyright......Page 5
Preface......Page 8
About the Authors......Page 10
Contents......Page 12
List of Figures......Page 20
List of Tables......Page 24
Notation......Page 26
Part I PROBABILISTIC REASONING......Page 28
1 Bayesian Reasoning......Page 30
2 Introducing Bayesian Networks......Page 56
3 Inference in Bayesian Networks......Page 80
4 Decision Networks......Page 116
5 Applications of Bayesian Networks......Page 144
Part II LEARNING CAUSAL MODELS......Page 174
6 Learning Linear Causal Models......Page 178
7 Learning Probabilities......Page 202
8 Learning Discrete Causal Structure......Page 224
Part III KNOWLEDGE ENGINEERING......Page 248
9 Knowledge Engineering with Bayesian Networks......Page 252
10 Evaluation......Page 290
11 KEBN Case Studies......Page 314
Appendix A......Page 342
Appendix B......Page 344
Index......Page 382
