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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