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

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