One of the few to offer a graduate-level introduction to Bayesian statistics for social scientists, this text provides detailed explanations of derivations and theories using a computationally oriented approach. This edition presents many new developments in the field, such as the deviance information criterion, hybrid MCMC algorithms, diagnostics, perfect sampling, and Bayesian nonparametrics. The author emphasizes applications commonly used in the social sciences, including regression models and covariates, and uses the most up-to-date MCMC package in WinBUGS and the new MCMCpack in R. He also supplies detailed advice and guidance on the mechanics of stochastic simulation.