The Annals of Applied Probability

A note on Metropolis-Hastings kernels for general state spaces

Luke Tierney

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The Metropolis-Hastings algorithm is a method of constructing a reversible Markov transition kernel with a specified invariant distribution. This note describes necessary and sufficient conditions on the candidate generation kernel and the acceptance probability function for the resulting transition kernel and invariant distribution to satisfy the detailed balance conditions. A simple general formulation is used that covers a range of special cases treated separately in the literature. In addition, results on a useful partial ordering of finite state space reversible transition kernels are extended to general state spaces and used to compare the performance of two approaches to using mixtures in Metropolis-Hastings kernels.

Article information

Ann. Appl. Probab., Volume 8, Number 1 (1998), 1-9.

First available in Project Euclid: 29 July 2002

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Zentralblatt MATH identifier

Primary: 60J05: Discrete-time Markov processes on general state spaces 65C05: Monte Carlo methods 62-04: Explicit machine computation and programs (not the theory of computation or programming)

Markov chain Monte Carlo Peskun's theorem mixture kernels


Tierney, Luke. A note on Metropolis-Hastings kernels for general state spaces. Ann. Appl. Probab. 8 (1998), no. 1, 1--9. doi:10.1214/aoap/1027961031.

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