Innovations in Bayesian Networks
Theory and Applications
Editors: Holmes, Dawn E. (Ed.)
Free Preview- Presents the innovative paradigms related to the theory and practical applications of Bayesian Networks
Buy this book
- About this book
-
Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained.
Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.
- Table of contents (12 chapters)
-
-
Introduction to Bayesian Networks
Pages 1-5
-
A Polemic for Bayesian Statistics
Pages 7-32
-
A Tutorial on Learning with Bayesian Networks
Pages 33-82
-
The Causal Interpretation of Bayesian Networks
Pages 83-116
-
An Introduction to Bayesian Networks and Their Contemporary Applications
Pages 117-130
-
Table of contents (12 chapters)
Recommended for you
Bibliographic Information
- Bibliographic Information
-
- Book Title
- Innovations in Bayesian Networks
- Book Subtitle
- Theory and Applications
- Editors
-
- Dawn E. Holmes
- Series Title
- Studies in Computational Intelligence
- Series Volume
- 156
- Copyright
- 2008
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-540-85066-3
- DOI
- 10.1007/978-3-540-85066-3
- Hardcover ISBN
- 978-3-540-85065-6
- Softcover ISBN
- 978-3-642-09875-8
- Series ISSN
- 1860-949X
- Edition Number
- 1
- Number of Pages
- X, 322
- Number of Illustrations
- 92 b/w illustrations
- Topics
