内容

目次

Part I. Fundamentals of Bayesian Inference: 1. Introduction; 2. Basicconcepts of probability and inference; 3. Posterior distributions andinference; 4. Prior distributions; Part II. Simulation: 5. Classicalsimulation; 6. Basics of Markov chains; 7. Simulation by MCMC methods; PartIII. Applications: 8. Linear regression and extensions; 9. Multivariateresponses; 10. Time series; 11. Endogenous covariates and sample selection;Appendix A: Probability distributions and matrix theorems; Appendix B:Computer programs for MCMC calculations.