Required:
- A First Course in Bayesian Statistical Methods by Peter D. Hoff.:
- Section 10.2: The Metropolis algorithm
- Section 10.3: The Metropolis algorithm for Poisson regression
- Section 10.4: Metropolis, Metropolis-Hastings and Gibbs
- Section 10.5: Combining the Metropolis and Gibbs algorithm
- Section 10.6: Discussion and further references
Optional:
- Bayesian Data Analysis (Third Edition) by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin.:
- Section 11.2: Metropolis and Metropolis-Hastings algorithms
- Section 11.3: Using Gibbs and Metropolis as building blocks