Customer Reviews

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

5 star:		(1)
4 star:		(2)
3 star:		(1)
2 star:		(0)
1 star:		(0)

 

 

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.

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.

Bayesian Artificial Intelligence, Second Edition by Kevin B. Korb and Ann E. Nicholson is among one of the very few books which explain the probabilistic graphical models and Bayesian belief networks in a balanced way; i.e. without making it a mathematical exercise in futility or by dumbing it down too much to make it a `practical guide'. This book is an interesting read and knowing the KDD genre, it's few and far between when one can say these words about a machine learning book.
Bayesian Artificial Intelligence is organized into three main sections; probabilistic reasoning, learning causal models and knowledge engineering. The book discusses Bayesian networks as a function of their usage i.e. for reasoning, learning and inference. Book begins with an introduction to Probabilistic Reasoning where authors discusses Bayesian reasoning, reasoning under uncertainty, uncertainty in artificial intelligence, probability calculus and other related concepts. Authors then provide a primer of Bayesian networks before discussing inference in Bayesian Networks. In the chapter titled applications of Bayesian network, authors elaborate on different types of applications and their practical implementations. In the second part authors focus on learning the causal models, learning the probabilities from datasets, Bayesian Network classifiers, learning linear causal models, learning discrete causal structure and so on. The third section concentrates on knowledge engineering with Bayesian Network; it has a long chapter which talks about different aspects of knowledge engineering for example KEBN life cycle, Bayesian network modeling, how Bayesian structure is build, kept and developed etc. Finally we see the case studies for different sections and the software packages associated with it.

I personally really enjoyed this book mainly because it's to the point, precise and well written. Due to the wide range of the field of machine learning and implementation of Belief networks, it becomes quite challenging to comprehensively cover the area. If you would like to read more about the general graphical models and probabilistic graphical models in machine learning, there are other texts out there however if your focus is Bayesian Artificial Intelligence and the belief networks, this book is quite useful.

The book is not written as a typical text book but still provides a set of problems at the end of each chapter. For theorem solvers and theory lovers, there are also various theoretical issues discussed in this book throughout related to the Bayesian provability and probability calculus. Overall it is not a so called `math heavy' or theorem proving text but rather quite practical introduction to Bayesian AI. I highly recommend this book if you would like to learn Bayesian AI, Bayesian belief networks, Bayesian inference, learning, reasoning or any pertaining disciplines.

The book by Korb and Nicholson is very readable and structured. Starting with some background information in statistics it comes directly to the major topic of the book - bayesian networks. The theory thereof is nicely evolved and applied to small examples to demonstrate its usage. Each chapter finishes with a short summary and bibliographical notes for further readings.

In my opinion this book is well written and the chosen examples are insightful. What I do not like is part three of the book which is devoted to case studies and praktical examples. If this space had been used for the first two parts by providing more details, e.g., for the discussion of path models (which is given but only short), this book could be even great on a more advanced level. In this form it is very good as an introduction in Bayesian Networks and related topics like the larger class of causal models.