Lecture 7: Adaptive Metropolis & Metropolis-Hastings

Readings

• A First Course in Bayesian Statistical Methods by Peter D. Hoff

  • Section 10.4: Metropolis, Metropolis-Hastings and Gibbs

  • Section 10.5: Combining the Metropolis and Gibbs algorithm

  • Section 10.6: Discussion and further references

  • Chapter 6: Posterior approximation with the Gibbs sampler

• 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 11.6: Example
  • Section 11.7: Bibliographic note

• An Adaptive Metropolis Algoritm by Heikki Haario, Eero Saksman, Johanna Tamminen (2001)

• Examples of Adaptive Metropolis by Gareth O. Roberts & Jeffrey S. Rosenthal (2009) Journal of Computational and Graphical Statistics, 18:2, 349-367, DOI: 10.1198/ jcgs.2009.06134

• The Bayesian Choice (Second Edition) by Christian Robert