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.
Author(s): Dawn E. Holmes, Lakhmi C. Jain (auth.), Prof. Dawn E. Holmes, Prof. Lakhmi C. Jain (eds.)
Series: Studies in Computational Intelligence 156
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2008
Language: English
Pages: 322
Tags: Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)
Front Matter....Pages I-X
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
Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer....Pages 131-167
Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks....Pages 169-185
An Information-Geometric Approach to Learning Bayesian Network Topologies from Data....Pages 187-217
Causal Graphical Models with Latent Variables: Learning and Inference....Pages 219-249
Use of Explanation Trees to Describe the State Space of a Probabilistic-Based Abduction Problem....Pages 251-280
Toward a Generalized Bayesian Network....Pages 281-288
A Survey of First-Order Probabilistic Models....Pages 289-317
Back Matter....Pages 319-321
