Understanding the Metropolis-Hastings Algorithm
- 1 November 1995
- journal article
- research article
- Published by Taylor & Francis in The American Statistician
- Vol. 49 (4) , 327-335
- https://doi.org/10.1080/00031305.1995.10476177
Abstract
We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions. A simple, intuitive derivation of this method is given along with guidance on implementation. Also discussed are two applications of the algorithm, one for implementing acceptance-rejection sampling when a blanketing function is not available and the other for implementing the algorithm with block-at-a-time scans. In the latter situation, many different algorithms, including the Gibbs sampler, are shown to be special cases of the Metropolis-Hastings algorithm. The methods are illustrated with examples.Keywords
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