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<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: &nbsp;</p><p>1. An Introduction to R&nbsp;&nbsp; 2. Introduction to Bayesian Thinking&nbsp;&nbsp; 3. Single-Parameter Models&nbsp;&nbsp; 4. Multiparameter Models&nbsp;&nbsp; 5. Introduction to Bayesian Computation 6. Markov Chain Monte Carlo Methods&nbsp;&nbsp; 7. Hierarchical Modeling&nbsp;&nbsp; 8. Model&nbsp;&nbsp;Comparison&nbsp;&nbsp; 9. Regression Models&nbsp;&nbsp; 10. Gibbs Sampling&nbsp;&nbsp; 11. Using R to Interface with WinBUGS </p><p></p><p>Website for the book:&nbsp; <a href="http://bayes.bgsu.edu/bcwr/">http://bayes.bgsu.edu/bcwr/</a></p><p></p><p></p>
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