Bayesian Computation with R (Use R!)

I have only read a few chapters of this book so far, but it relies heavily and easy-to-grasp examples to illustrate the subject matter. It does not give much background, so this is definitely more of a supplemental book, though that is what I expected upon purchasing. I will update my rating after reading more.

Great book - good concrete examples. It COULD, IMHO, have gone farther into using Bayesian methods with regression models and how the likelihood function for the posterior is generated. It also needs solutions to the chapter exercises in order to improve its utility as a tool for self-education.

The good: The first three chapters gives the reader a nice introduction to using R for Bayesian statistics and some well worked out examples: a necessity when dealing with a program that one is unfamiliar with. The text does a decent job of complementing the material found in another text on basic Bayesian methodology such as Gelman et al. (2004) or Carlin and Lewis (2008). Furthermore, Jim Albert is a great writer and presents the material well.The Facts: Towards the latter half of the text the author begins to use a program from the 'Learn Bayes' package entitled "Laplace". It is of my belief that this black box could be elaborated on some. I had some trouble getting many of the examples from the text as well as exercises from the sections to run simply because of this black box. None of nine graduate students working together and independently were able to get this function to perform its duties on a regular basis. However, the examples and problems were instructive.The Opinion: I was not a fan of the functions from the Learn Bayes package and did not feel as though the reader gained an adequate background on how to program R to perform Bayesian methods on his/her own. The book, I believe, relied to much (in the latter half of the text) on the functions of the Learn Bayes package.Overall the text is great resource to complement another text. The only real `issue' I had with this text was not the text itself but rather the "Learn Bayes" package. If you are looking for a resource for R this might not be the right book. As a quick and dirty introduction to Bayesian methods using R (as the title suggests) this isn't a BAD text.

I didn't know the package number, so I couldn't search where my book is. However, it was perfectly arrived.

Excellent Short Intro to Subject - starts simple, introducing R and then moves onto the more complicated cases, including WinBUGS

Not as good as many of the others in the use R! series, this book definitely has a split personality and the first few chapters do not logically lead into the second half. Even though this is not meant as a stand alone text, I would still hesitant to recommend it to anyone not already well versed in Bayesian Inference.

Full of examples and codes.Really helpedalso LearnBayes package SO helpfulstrong recommending to buy

Great book. If you work through the examples, this book will move you to very near the top of the R learning curve and, more importantly, race you to the peak of the Bayesian curve.

This extensive (over 200 pages) book is intended to assist the use of the R programming language for Bayesian statistical calculations.It takes a step-by-step approach, using straightforward practical examples immediately.It introduces built-in R functions appropriate to each specific example and reviews them briefly at the end of each one.It starts with some simple standard descriptive statistical examples and includes useful graphical R plotting functions to display helpful diagrams. It assumes you have intermediate statistical knowledge and can move beyond the simple descriptive stats work with relative ease. It then uses these concepts to introduce Bayesian statistics after a very short introduction. In effect, you should already be aware of the principles of the Bayesian approach (initial belief modelled via prior distributions, these modified through measurements expressed in posterior distributions which are then used to draw inferences). I had some of this knowledge, but swiftly realised I would need to devote serious study to grasp it thoroughly. This is an excellent book, with an extensive bibliography, to help you along the way.

Very good! Useful manual to apply bayesian approach to data mining. Incredible the price!It is a product for advanced statistician.