This book provides a compact selfcontained introduction to the theory andapplication of Bayesian statistical methods. The book is accessible to readerswith only... » 

This book provides a compact selfcontained introduction to the theory andapplication of Bayesian statistical methods. The book is accessible to readerswith only a basic familiarity with probability yet allows more advancedreaders to quickly grasp the principles underlying Bayesian theory and methods.The examples and computer code allow the reader to understand and implementbasic Bayesian data analyses using standard statistical models and to extendthe standard models to specialized data analysis situations. The book beginswith fundamental notions such as probability exchangeability and Bayes ruleand ends with modern topics such as variable selection in regressiongeneralized linear mixed effects models and semiparametric copula estimation.Numerous examples from the social biological and physical sciences show how toimplement these methodologies in practice. Monte Carlo summaries of posteriordistributions play an important role in Bayesian data analysis. The opensourceR statistical computing environment provides sufficient functionality to makeMonte Carlo estimation very easy for a large number of statistical models andexample Rcode is provided throughout the text. Much of the example code can berun as is in R and essentially all of it can be run after downloading therelevant datasets from the companion website for this book. TOCIntroductionand examples. Belief probability and exchangeability. One parameter models.Monte Carlo approximation. The normal model. Posterior approximation with theGibbs sampler. The multivariate normal model. Group comparisons andhierarchical modeling. Linear regression. Nonconjugate priors and theMetropolisHastings algorithm. Linear and generalized linear mixed effectsmodels. Latent variable methods for ordinal data. «