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Bayesian Computation with R
There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large numb ...more
Paperback, 267 pages
Published July 1st 2007 by Springer
(first published June 11th 2007)
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A well written guide to Bayesian R computation, one thing which really bugged me was the fact that the list of commands were given at the end of each chapter thus it can be confusing at first because we are not told what the commands are meant to do. A lot of the ideas could also be elaborated more too.
An excellent first five chapters which are pretty well documented and have nice code that works at his website. From there the quality is widely considered to drop off. I personally found the exercises a good way to learn basic things about the techniques and trade-offs involved in sampling from multivariate probability distributions.
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