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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.
Jim Albert is Professor of Statistics at Bowling Green State University. He is Fellow of the American Statistical Association and is past editor of The American Statistician. His books include Ordinal Data Modeling (with Val Johnson), Workshop Statistics: Discovery with Data, A Bayesian Approach (with Allan Rossman), and Bayesian Computation using Minitab.--This text refers to an alternate Paperback edition.
Full of examples and codes.
also LearnBayes package SO helpful
strong recommending to buy
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... Read morePublished 7 months ago by George H. Avery
Excellent Short Intro to Subject - starts simple, introducing R and then moves onto the more complicated cases, including WinBUGSPublished 19 months ago by email@example.com
Some reviews complain about the use of learbays package in the book. We can actually read the source codes of the package, and I found this a good way to learn R and Bayesian... Read morePublished 22 months ago by Stephen C
I was hoping for a good, solid, book explaining Bayesian methods in R.
This book has 2-problems though:
1. Makes assumptions on what you already know. Read more
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. Read morePublished on November 1, 2013 by D. Fedak
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. Read morePublished on August 18, 2013 by Ian Goldstein
Recommended by the Revolution R blog. Would like to get it for Kindle. When will it be available again? One star because it's not available for Kindle!Published on May 4, 2013 by D. T. Smith