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Bayesian programming
Bessière P., Mazer E., Ahuactzin J., Mekhnacha K., Chapman & Hall/CRC, Boca Raton, FL, 2014. 380 pp. Type: Book (978-1-439880-32-6)
Date Reviewed: Aug 12 2014

Bayesian programming uses plausible reasoning to extend logic to cases where the premises are not certain. A complete system for efficient problem solving needs modeling, inference algorithms, new programming languages, and new hardware. The authors focus on modeling and inference algorithms, and use current languages and hardware to provide examples. They take pains to clearly state the goal of each chapter and use specific concrete examples as illustrations, which greatly help in understanding. They provide a Python package for an extensive library used in the examples. The programs from the book run as is and may be extended if desired.

The introductory chapter presents probability as an alternative to logic. Chapters 2 to 11 present a new modeling methodology. Chapters 2 to 6 cover Bayesian programming, while chapters 7 to 11 show how to combine elementary Bayesian programs. Chapters 12 to 15 give a collection of inference algorithms. Finally, because of the controversy the subjectivist approach sometimes arouses, chapter 16 discusses frequently asked questions and frequently argued matters, and chapter 17 contains an extensive glossary.

A spam filter illustrates the basic concepts presented in chapter 2. A water treatment unit in chapter 3 nicely illustrates the effects of incompleteness and uncertainty. In chapter 4, a small mobile robot either pushes objects or follows their contours, illustrating the notion of description. Chapter 6 continues the water treatment example. The notation, especially in the later chapters, can be a bit cumbersome, but the concrete examples, figures, and Python programs make it possible to interpret formulas in context if desired.

The authors are convinced that “25 years from now, the ability to treat incomplete and uncertain data will be as inescapable for computers as graphical abilities are today.” They have done a really fine job presenting Bayesian programming for those interested in proceeding in that direction.

Reviewer:  Arthur Gittleman Review #: CR142614 (1411-0939)
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