Innovations in Bayesian Networks: Theory and Applications

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Dawn E. Holmes
Springer Science & Business Media, Oct 2, 2008 - Mathematics - 322 pages
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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.

 

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Contents

Introduction to Bayesian Networks
1
A Polemic for Bayesian Statistics
6
A Tutorial on Learning with Bayesian Networks
33
The Causal Interpretation of Bayesian Networks
83
An Introduction to Bayesian Networks and Their Contemporary Applications
117
Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer
131
Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks
169
An InformationGeometric Approach to Learning Bayesian Network Topologies from Data
186
Causal Graphical Models with Latent Variables Learning and Inference
219
Use of Explanation Trees to Describe the State Space of a ProbabilisticBased Abduction Problem
250
Toward a Generalized Bayesian Network
281
A Survey of FirstOrder Probabilistic Models
289
Author Index
318
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