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 selfcontained 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 system. A researcher, on the other hand, is interested in an introduction to the theoretical foundations and the basic algorithms in the field of Bayesian networks. A third possible kind of reader would be the domain expert. She may be an engineer, a physician, or a social scientist who wishes to learn about Bayesian networks in order to use them in her domain. The authors do not claim to have this kind of reader in mind, but I can recommend the book to them as well. For the first two kinds of readers, I would recommend two different tracks through the book. Although they are not indicated formally in the book, I shall comment separately on the tracks as I see them.
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