A First Course in Bayesian Statistical Methods [Paperback]

1. A self-contained introduction to probability, exchangeability and Bayes rule provides a theoretical understanding of the applied material.

2. Numerous examples with R-code that can be run as-is allow the reader to perform the data analyses themselves.

3. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

This compact, self-contained introduction to the theory and application of Bayesian statistical methods is accessible to those with a basic familiarity with probability, yet allows advanced readers to grasp the principles underlying Bayesian theory and method.

and examples.- Belief, probability and exchangeability.- One-parameter models.- Monte Carlo approximation.- The normal model.- Posterior approximation with the Gibbs sampler.- The multivariate normal model.- Group comparisons and hierarchical modeling.- Linear regression.- Nonconjugate priors and Metropolis-Hastings algorithms.- Linear and generalized linear mixed effects models.- Latent variable methods for ordinal data.

From the reviews:

This is an excellent book for its intended audience: statisticians who wish to learn Bayesian methods. Although designed for a statistics audience, it would also be a good book for econometricians who have been trained in frequentist methods, but wish to learn Bayes. In relatively few pages, it takes the reader through a vast amount of material, beginning with deep issues in statistical methodology such as de Finettis theorem, through the nitty-gritty of Bayesian computation to sophisticated models such as generalized linear mixed effects models and copulas. And it does so in a simple manner, always drawing parallels and contrasts between Bayesian and frequentist methods, so as to allow l³.