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Academic & Professional Books  Reference  Data Analysis & Modelling  Data Analysis & Statistics

Bayesian Computation with R

Handbook / Manual
Series: Use R!
By: Jim Albert
267 pages, Figs, tabs
Publisher: Springer Nature
Bayesian Computation with R
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  • Bayesian Computation with R ISBN: 9780387922973 Edition: 2 Paperback May 2009 Usually dispatched within 1-2 weeks
Price: £54.99
About this book Contents Customer reviews Related titles

About this book

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. Also the book 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.


An introduction to R.- Introduction to Bayesian thinking.- Single parameter models.- Multiparameter models.- Introduction to Bayesian computation.- Markov chain Monte Carlo methods.- Hierarchical modeling.- Model comparision.- Regression models.- Gibbs sampling.- Using R to interface with WinBUGS.

Customer Reviews

Handbook / Manual
Series: Use R!
By: Jim Albert
267 pages, Figs, tabs
Publisher: Springer Nature
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