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. Multivariate responses
10. Time series
11. Endogenous covariates and sample selection
¢ºAppendix
|