Statistics and Computing

, Volume 26, Issue 4, pp 761–781 | Cite as

A computational procedure for estimation of the mixing time of the random-scan Metropolis algorithm

  • David A. SpadeEmail author


Many situations, especially in Bayesian statistical inference, call for the use of a Markov chain Monte Carlo (MCMC) method as a way to draw approximate samples from an intractable probability distribution. With the use of any MCMC algorithm comes the question of how long the algorithm must run before it can be used to draw an approximate sample from the target distribution. A common method of answering this question involves verifying that the Markov chain satisfies a drift condition and an associated minorization condition (Rosenthal, J Am Stat Assoc 90:558–566, 1995; Jones and Hobert, Stat Sci 16:312–334, 2001). This is often difficult to do analytically, so as an alternative, it is typical to rely on output-based methods of assessing convergence. The work presented here gives a computational method of approximately verifying a drift condition and a minorization condition specifically for the symmetric random-scan Metropolis algorithm. Two examples of the use of the method described in this article are provided, and output-based methods of convergence assessment are presented in each example for comparison with the upper bound on the convergence rate obtained via the simulation-based approach.


Markov chain Monte Carlo Mixing time Drift and minorization Bayesian inference Computational statistics 



The author would like to thank Radu Herbei and Laura Kubatko, as well as two anonymous reviewers, for their helpful insights and commentary during the completion of this project.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.University of Missouri–Kansas CityKansas CityUSA

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