This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book.
From the reviews:
"The matching of each computational technique to a real data set allows readers to fully appreciate the Bayesian analysis process, from model formation to prior selection and practical implementation." (Lawrence Joseph from Biometrics, Issue 63, September 2007)
"Recent times have seen several new books introducing Bayesian computing. This book is an introduction on a higher level. ‘The purpose of this book is to provide a self-contained entry to practical & computational Bayesian Statistics using generic examples from the most common models.’ ... Many researchers and Ph.D. students will find the R-programs in the book a nice start for their own problems and an innovative source for further developments." (Wolfgang Polasek, Statistical Papers, Vol. 49, 2008)
"This text intentionally focuses on a few fundamental Bayesian statistical models and key computational tools. ... Bayesian Core is more than a textbook: it is an entire course carefully crafted with the student in mind. ... As an instructor of Bayesian statistics courses, I was pleased to discover this ready- and well-made, self-contained introductory course for (primarily) graduate students in statistics and other quantitative disciplines. I am seriously considering Bayesian Core for my next course in Bayesian statistics." (Jarrett J. Barber, Journal of the American Statistical Association, Vol. 103 (481), 2008)
"The book aims to be a self-contained entry to Bayesian computational statistics for practitioners as well as students at both the graduate and undergraduate level, and has been test-driven in a number of courses given by the authors. ... Two particularly attractive aspects of the book are its concise and clear writing style, which is really enjoyable, and its focus on the development of an intuitive feel for the material: the numerous insightful remarks should make the book a real treat ... ." (Pieter Bastiaan Ober, Journal of Applied Statistics, Vol. 35 (1), 2008)
"The book is a good, compact and self-contained introduction to the applications of Bayesian statistics and to the use of R to implement the procedures. ... a reader with a previous formal course in statistics will enjoy reading this book. ... the authors are not shy of presenting such complex models as hidden Markov models and Markov random fields in a simple and direct way. This adds an edge to a compact and useful text." (Mauro Gasparini, Zentralblatt MATH, Vol. 1137 (15), 2008)
"This book’s title captures its focus. It is a textbook covering the core statistical models from both a Bayesian viewpoint and a computational viewpoint. ... There is a discussion of choice of priors, along with math to derive the priors. ... The book is being actively used as a textbook by a number of university courses. ... The course level is graduate or advanced undergraduate. Solutions to the exercises are available to course instructors ... . In conclusion, the book does what it does, well." (Rohan Baxter, ACM Computing Reviews, December, 2008)
