Bayesian Artificial Intelligence (Chapman & Hall/CRC Computer Science and Data Analysis)

Rating:
Summary: Bayesian Networks for Undergrads and Practicioners
Review: Despite its name "Bayesian Artificial Intelligence" covers Bayesian network (BN) techniques only. Other Bayesian techniques useful for AI are not treated.
The content is divided in three main sections: (1) The basics of probabilistic reasoning with BNs, (2) Causal discovery (finding BNs from data), and (3) "Knowledge engineering".
The first part covers the fundamental concepts and algorithms around BNs and (simple) decision networks. It is well-written and clear, but readers who are not totally new to the field might find only little new information (e.g., loopy belief propagation, continuous densities, large decision networks, etc. are not covered).
The second part is on how to deduce causal relationships from observational data. Constrained-based and Bayesian approaches are covered, but on a rather general level. I am not sure how easy it is to implement the algorithms from the descriptions provided. When it comes to details of the algorithms, proofs, or mathematical background the authors very often refer to the literature due to "lack of space". From a practical standpoint, it is unfortunate that the different methods are compared to each other only superfiscially. For instance, one method presented performs a large number of statistical tests; one would expect that this requires large amounts of data in order to avoid false positive results. Is this a problem? With questions like these the reader is often left alone.
I am not competent to talk about part three (knowledge engineering), so I end with my general impression of the book: I would have appreciated if the authors had treated some the algorithms in greater detail and had spent a few pages on advanced concepts and current research directions. On the other hand, some information provided could have easily been left out. (For instance, how to download and install certain software packages from the internet, Kevin Murphy's well-known survey on BN software packages, screenshots of user dialogs, etc. just eat pages. Providing the URLs to the corresponding sites on the internet is completely sufficient, and the information there is more likely to be up-to-date.) The saved pages could then be spent on information which is not readily available elsewhere.
To summarize: The book provides a mostly well-written general overview of the basic concepts and could serve as a first introduction to the field. However, it leaves the reader often alone when it comes to the mathematical background, potential practical pittfalls, or advanced algorithms.

Rating:
Summary: Excellent Introductory Text
Review: It is difficult to assess a review without understanding the biases of the reviewer. I fall under the category of researcher/practitioner when it comes to reasoning with graphical models. I am familiar with and make use of several books and papers on this topic in my work. Of the set of standard references (Pearl, Jensen, Neapolitan, Jordan, Cowell et al., Borgelt & Kruse) the text by Korb and Nicholson (K&N) stands out in terms of its clarity and accessibility. Does the book have everything one would ever want to know about Bayesian inference? Not by a long shot. Is it, however, a good place to start? Definitely. The basic concepts are presented relatively completely and with clarity. I consistently recommend K&N over other alternatives to colleagues new to the field. Is there a chasm separating concept and algorithm in the book? I don't think there is, especially relative to other references. With tools such as Kevin Murphy's BNT, or Netica available on the Web, it seems to me that providing a solid conceptual framework becomes paramount for a textbook such as this. I believe K&N succeed admirably in this sense. Why four stars and not five? Even for an introductory text such as K&N, it would be nice to have more development of some concepts such as causality, context specific independence, or loss of independence in dynamic nets. Although it won't be your last book on reasoning with graphical models, K&N should probably be your first.