<p><br/></p><p>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.</p><p>Table of Contents:  </p><p>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 </p><p></p><p>Website for the book:  <a href="http://bayes.bgsu.edu/bcwr/">http://bayes.bgsu.edu/bcwr/</a></p><p></p><p></p>