Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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)
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