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Pattern Analysis and Applications

, Volume 7, Issue 2, pp 221–223 | Cite as

Bayesian Artificial Intelligence

Kevin B. Korb, Ann E. Nicholson, Chapman & Hall, 2004, 354 pages
  • Finn V. Jensen
Book Review

Introduction

Kevin Korb and Ann Nicholson are experienced researchers in Bayesian networks. They have contributed to the theoretical development of the field, and they have several application projects behind them. This is apparent in their textbook, Bayesian Artificial Intelligence. It is a well written introduction to the field, and it contains many useful guidelines for building Bayesian network models. You cannot be successful in this field without a good insight into the mathematical theory behind it, and the book provides a smooth and self-contained presentation.

In the preface, the authors state that the book is aimed at advanced undergraduates in computer science and those who wish to engage in pure research in Bayesian network technology. These are two kinds of readers. The first kind I shall call a practitioner. A practitioner is interested in learning sufficient material on the topic so as to be in a position to assist a domain expert in constructing a Bayesian network...

Keywords

Bayesian Network Domain Expert Dynamic Bayesian Network Bayesian Network Model Junction Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark

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