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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.

A First Course in Bayesian Statistical Methods

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.