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June 2003 Self-regenerative Markov chain Monte Carlo with adaptation
Sujit K. Sahu, Anatoly A. Zhigljavsky
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Bernoulli 9(3): 395-422 (June 2003). DOI: 10.3150/bj/1065444811


A new method of construction of Markov chains with a given stationary distribution is proposed. The method is based on constructing an auxiliary chain with some other stationary distribution and picking elements of this auxiliary chain a suitable number of times. The proposed method is easy to implement and analyse; it may be more efficient than other related Markov chain Monte Carlo techniques. The main attractive feature of the associated Markov chain is that it regenerates whenever it accepts a new proposed point. This makes the algorithm easy to adapt and tune for practical problems. A theoretical study and numerical comparisons with some other available Markov chain Monte Carlo techniques are presented.


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Sujit K. Sahu. Anatoly A. Zhigljavsky. "Self-regenerative Markov chain Monte Carlo with adaptation." Bernoulli 9 (3) 395 - 422, June 2003.


Published: June 2003
First available in Project Euclid: 6 October 2003

zbMATH: 1044.62033
MathSciNet: MR1997490
Digital Object Identifier: 10.3150/bj/1065444811

Keywords: adaptive method , Bayesian inference , Metropolis-Hastings algorithm , regeneration

Rights: Copyright © 2003 Bernoulli Society for Mathematical Statistics and Probability

Vol.9 • No. 3 • June 2003
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