practical Bayesian inference or computer-intensive methods, or ... flect specialist areas that require high performance ... Distributions with Bounded Support and.
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Book Reviews
technique and interpretation is explained, although a preference for the Bayesian approach is indicated. Three chapters are devoted to the analysis of microarrays, with detailed descriptions of preprocessing methods, and on building two types of regulatory networks: a Bayesian multinomial network based on a differential equations model and a dynamic Gaussian Bayesian state space network. One chapter is dedicated to a review of available software for implementing probabilistic models that are described in the book. In some chapters the subjective nature of the Bayesian approach and a particular form of the solution as its consequence are not always given sufficient emphasis. Generally, it is a refreshing book for a statistician, with occasionally different but recognizable terminology, and giving a good description of a wide variety of complex models.
esting part of the book. The applications tend to reflect specialist areas that require high performance computing. Chapter 15 includes some discussion of the importance of hiding the details of the computation from the statistician, a point that is underemphasized in earlier chapters. Parallel pseudorandom-number generation is also considered in detail in later chapters, including the interesting chapter about parallel Markov chain Monte Carlo implementation. This is a well-written collection, with extensive bibliographic material, and, importantly, a very good index. The Editor has certainly satisfied his first objective, by providing an excellent state of the art review. The interface between parallel and statistical computation is immature, as reflected by the lack of general purpose statistical software, but this book does represent a step in the right direction.
Natalia Bochkina Imperial College London
Niall Adams Imperial College London
Handbook of Parallel Computing and Statistics E. J. Kontoghiorghes (ed.), 2006 Boca Raton, Chapman and Hall–CRC 552 pp., $119.95 ISBN 0-824-74067-X Statistical areas that require high performance computing are either inherently computational, like practical Bayesian inference or computer-intensive methods, or are concerned with processing very large data sets, like information retrieval and data mining. I work in the latter area, so I was interested to see whether this book satisfied the Editor’s twofold objectives: (a) to provide an up-to-date review of parallel computing and (b) to develop the interface between statistical and parallel computation. This edited collection is divided into three parts. The first part of the book is a general introduction to parallel computing, concerned with technical computing issues, including parallel computer architecture, parallel software tools and implementation of important linear algebra methods. Consideration of linear algebra pervades the entire book—this is hardly surprising given the central role of linear algebra in statistical computation. The second part is concerned with parallel optimization. Here we see more of the subtlety and ingenuity that are deployed in the design and implementation of parallel algorithms. The final part of the collection is concerned with statistical applications. For me, this is the most inter-
Beyond Beta: Other Continuous Families of Distributions with Bounded Support and Applications S. Kotz and J. R. van Dorp, 2004 Singapore, World Scientific xvi + 290 pp., £35 ISBN 9-812-56115-3 This monograph is meant to complement Gupta and Nadarajah (2004) and is a comprehensive survey of univariate continuous distributions on a bounded domain. It includes chapters on the triangular distribution and its generalizations, two-sided power distributions, generalized trapezoidal distributions, uneven two-sided power distributions, the reflected Topp and Leone distribution, and a general framework for two-sided distributions; it concludes with appendices on the relationships between these distributions, and on Johnson’s SB class of distributions with bounded domain. It contains several data analyses, mostly from problems in engineering or econometrics. There is a Web site (which is not mentioned in the book) with these data sets as well as software that are useful for calculating maximum likelihood estimates for the models. Most of the book concentrates on working out the basic properties of bounded domain distributions (e.g. moments) and on obtaining maximum likelihood estimates, though there is no attempt to evaluate their variability. Another area which might have been of interest for applied statisticians and is left unmentioned is how to fit models including covariates.
