Part I. Fundamentals of Bayesian Inference 1. Introduction 2. Basic concepts of probability and inference 3. Posterior distributions and inference 4. Prior distributions
Part II. Simulation 5. Classical simulation 6. Basics of Markov chains 7. Simulation by MCMC methods
Part III. Applications 8. Linear regression and extensions 9. Semiparametric regression 10. Multivariate responses 11. Time series 12. Endogenous covariates and sample selection
A. Probability distributions and matrix theorems B. Computer programs for MCMC calculations