Bayesian Networks: An Introduction

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John Wiley & Sons, Aug 26, 2011 - Mathematics - 366 pages
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Bayesian Networks: An Introduction provides a self-containedintroduction to the theory and applications of Bayesian networks, atopic of interest and importance for statisticians, computerscientists and those involved in modelling complex data sets. Thematerial has been extensively tested in classroom teaching andassumes a basic knowledge of probability, statistics andmathematics. All notions are carefully explained and featureexercises throughout.

Features include:

  • An introduction to Dirichlet Distribution, Exponential Familiesand their applications.
  • A detailed description of learning algorithms and ConditionalGaussian Distributions using Junction Tree methods.
  • A discussion of Pearl's intervention calculus, with anintroduction to the notion of see and do conditioning.
  • All concepts are clearly defined and illustrated with examplesand exercises. Solutions are provided online.

This book will prove a valuable resource for postgraduatestudents of statistics, computer engineering, mathematics, datamining, artificial intelligence, and biology.

Researchers and users of comparable modelling or statisticaltechniques such as neural networks will also find this book ofinterest.

 

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Contents

Preface
Conditional
Evidence sufficiency andMonte Carlo methods 3 1 Hard evidence 3 2 Soft evidenceand
8
10
Factor graphs andthe sumproduct algorithm
References
Index
Copyright

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About the author (2011)

Timo Koski, Professor of Mathematical Statistics, Department of Mathematics, Royal Institute of Technology, Stockholm, Sweden.

John M. Noble, Department of Mathematics, University of Linköping, Sweden.

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