From the reviews:

The book is a concise presentation of a wide range of Bayesian inferential problems and the computational methods to solve them. The detailed and thorough presentation style, with complete R code for the examples, makes it a welcome companion to a theoretical text on Bayesian inference.... Smart students of statistics will want to have both R and Bayesian inference in their portfolio. Jim Albert's book is a good place to try out R while learning various computational methods for Bayesian inference. (Jouni Kerman, Teh American Statistician, February 2009, Vol. 63, No.1)

„This is a compact text, with 11 chapters. Overall it is well written and contains a plethora of interesting examples … . Each chapter ends with short notes on further reading, a summary of R commands that are introduced, and a collection of excellent exercises to test understanding of the material. … this book would be a useful companion to an introductory Bayesian text in a classroom setting or as a primer on R for a Bayesian practitioner.“ (John Verzani, SIAM reviews, Vol. 50 (4), December, 2008)

„This textbook is a compact introduction to modern computational Bayesian statistics. Without caring too much about mathematical details, the author gives an overall view of the main problems in statistics … . The examples and the applications provided are intended for a general audience of students.“ (Mauro Gasparini, Zentralblatt MATH, Vol. 1160, 2009)

“A book about Bayesian computation is highly welcome. … The book contains many interesting examples and is especially stimulating for students who start writing their own Bayesian programs. … This book serves this demand of students perfectly. … Thus, the book can be highly recommended for all introductory Bayes courses, preferably if the students had a statistics course with an introduction to R (or Splus) before.” (Wolfgang Polasek, Statistical Papers, Vol. 52, 2011)