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The authors, Marin from Universite Paris-Sud, and Robert, from Universite Paris-Dauphine, have attempted to produce an intermediate level text on Bayesian statistics aimed for use by graduate students who need to have a fundamental understanding of Bayesian methods for their research. The authors also intend the book to be useful to scientists in all disciplines who may need to use Bayesian statistics. For several years both authors have taught a course corresponding to the text as part of a second year masters program for students in data processing as well as in statistics. The aim is to have readers learn to think about the purpose of a Bayesian analysis and to develop an intuition of what their analytic results should look like. Exercises are found throughout the text, rather than at the end of each chapter. The hope is to have readers get a more immediate feedback about what they are learning as they progress through the material. This way, inaccuracies in thinking can be caught early in the learning process. My impression of the text is that it is more of a handbook on Bayesian methods than a traditional textbook. The writing style is generally clear and to the point. The subject matter is for the most part developed in a step by step manner, with an effort taken to clarify major concepts and equations as they develop in the discussion. The authors have chosen to employ R for all examples, and even have a section in the text entitled, “A short introduction to R.” However, the use of examples showing R code in the text is for the most part dropped after Chapter 4.

Bayesian Core: A Practical Approach to Bayesian Computational Statistics