Includes bibliographical references (p. [259]-262) and index.

1. An introduction to R -- 2. Introduction to Bayesian thinking -- 3. Single-parameter models -- 4. Multiparameter models -- 5. Introduction to Bayesian computation -- 6. Markov chain Monte Carlo methods -- 7. Hierarchical modeling -- 8. Model comparison -- 9. Regression models -- 10. Gibbs sampling -- 11. Using R to interface with WinBUGS.

""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." -- Cover.