 |
|
Title: Bayesian Computation with R (Use R)
ISBN: 0387713840
Author:
Jim Albert
Publicate Date: 2008-06-11
Publish: 2008-06-11
List Price: $49.95
Average Customer Rating: 4.0
Format: Paperback
 |
|
|
|
|
Amazon Lowest New Price: $39.04
Amazon Lowest Used Price: $36.00
Amazon Merchant Price: $44.95
|
|
|
|
|
|
|
|
|
|
|
| Customer Review: |
|
1: Another R Book
First 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).
The Bad: 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 is faulty and very badly behaved. Many of the examples from the text as well as exercises from the sections would not 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.
The Ugly: Use of a black box programming technique does nothing more than to add to confusion of how Bayesian methodology works and does not give the reader an adequate background on how to program R to perform Bayesian methods. Black box usage trains a budding statistician to point, click, type, and look at results without really giving them the necessary tools to know if what they are view is even reasonable or what they wanted in the first place.
Conclusions: Decent at first; weak at the last. Would not purchase again as a reference to R.
|
2: Fantastic Resource
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.
|
3: more practicality added to Bayesian inference
Jim Albert is a great teacher and an excellent writer. The R language is becoming one of the most used languages by statistical researchers. This is because it has many similarities to S and can be used freely, Jim makes R easy to learn for statisticians in this book. One of the big breakthroughs in Bayesian statistics over the past 2 decades was the implementation of complicated priors and hierarchical models through the Markov Chain Monte Carlo (MCMC) algorithms. The leaders is this filed created free software called BUGS (for Bayesian Analysis Using Gibbs Sampling). Gibbs sampling is one of the most commonly used MCMC algorithms. Statisticians using this software have been able to provide more satisfactory solutions to many basic and complex problems using these tools. After Windows became the dominant operating system on personal computers WINBUGS was born. This is a version of BUGS that uses Windows as the operating system and takes advantage of Windows many nice features. Now for the first time to my knowledge Jim Albert show the reader how to incorporate the BUGS technology in the framework of R programming. This can only add to the practical use of Bayesian methods among statisticians for research that advances both the theory and applications. In the late 1990s I was working in the medical device industry where a number of clinical trials were being analyzed using the MCMC methods. Jim deserves a great deal of credit for moving Bayesian statistics into the framework of R!
|
|
|
|
|
