Lecture 6: Introduction to Metropolis Algorithms and Diagnostics
Readings
- A First Course in Bayesian Statistical Methods by Peter D. Hoff.:
- Section 10.1: Generalized linear models
- Section 10.2: The Metropolis algorithm
- Section 10.3: The Metropolis algorithm for Poisson regression
- Section 10.1: Generalized linear models
- 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 11.4 Inference and Assessing Convergence
- Section 11.5 Effective number of simulations
- Section 11.6 Example
- The Bayesian Choice (Second Edition) by Christian Robert
See also Dunson, D. & Johndrow, J. (2020) The Hastings algorithm at fifty. Biometrika,107: pp. 1–23