Readings
- A First Course in Bayesian Statistical Methods by Peter D. Hoff
- Chapter 6: Posterior approximation with the Gibbs sampler
- Section 9.1: The linear regression model (review)
- Section 9.2: Bayesian estimation for a regression model
- Section 10.4: Metropolis, Metropolis-Hastings and Gibbs
- Section 10.5: Combining the Metropolis and Gibbs algorithm
- Section 10.6: Discussion and further references
- Section 12.1 Latent Variables for Ordinal Data
- Bayesian Data Analysis (Third Edition) by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
- Section 11.1: Gibbs sampler
- Section 11.2: Metropolis and Metropolis-Hastings algorithms
- Section 11.3: Using Gibbs and Metropolis as building blocks
- Section 11.7: Bibliographic note
- Section 14.1: Conditional modeling
- Section 14.2: Bayesian analysis of the classical regression model
- Albert & Chib Bayesian Analysis of Binary and Polychotomous Response Data, Journal of the American Statistical Association, Vol. 88, No. 422 pp. 669- 679 )
- The Bayesian Choice (Second Edition) by Christian Robert