Bayesian Computation with R (Use R)
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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 number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner’s g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package.
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13 of 13 people found the following review helpful
Format:Paperback
If you are familiar with R and with Bayesian Computation this book may be a good introduction to using R packages for Bayesian Computation, but I didn't feel it is a good introduction to Bayesian Computation or to R if you are unfamiliar with either or both.
6 of 7 people found the following review helpful
Format:Paperback
First two chapters are easy to follow, after that its down-hill. A lot of equations are stated without an explanation, no clear expositions. Writer falls into the trap of writing a book for himself and not the student. Nothing is explained clearly. No one in the 'real' world uses R, only mathematics departments in Universities, so you want be applying these functions in work related environments. No answers given to chapter questions. The book is called 'Bayesian computation in R', only if you understand Bayesian computation very well and need to see some examples in R. Otherwise your not going to learn Bayesian stats with this book.
1 of 4 people found the following review helpful
Format:Paperback
I love this book. It is rather condensed and technical and you will likely need a more general intro to Bayesian stats to accompany it, but that being said it's a really neat book.
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