A First Course in Bayesian Statistical Methods

2. Introduction and examples
Peter D. Hoff

3. Belief, probability and exchangeability
Peter D. Hoff

4. One-parameter models
Peter D. Hoff

5. Monte Carlo approximation
Peter D. Hoff

6. The normal model
Peter D. Hoff

7. Posterior approximation with the Gibbs sampler
Peter D. Hoff

8. The multivariate normal model
Peter D. Hoff

9. Group comparisons and hierarchical modeling
Peter D. Hoff

10. Linear regression
Peter D. Hoff

11. Nonconjugate priors and Metropolis-Hastings algorithms
Peter D. Hoff

12. Linear and generalized linear mixed effects models
Peter D. Hoff

13. Latent variable methods for ordinal data
Peter D. Hoff

Keywords: Statistics, Data Mining and Knowledge Discovery, Operations Research/Decision Theory, Econometrics, Probability and Statistics in Computer Science, Methodology of the Social Sciences, Statistical Theory and Methods