Self-regenerative Markov chain Monte Carlo with adaptation
Self-regenerative Markov chain Monte Carlo with adaptation
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.
adaptive method, Bayesian inference, independence sampler, Metropolis–Hastings algorithm, regeneration
395-422
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Zhigljavsky, Anatoly A.
5502a431-c58e-4746-95f0-99191b535a6d
2003
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Zhigljavsky, Anatoly A.
5502a431-c58e-4746-95f0-99191b535a6d
Sahu, Sujit K. and Zhigljavsky, Anatoly A.
(2003)
Self-regenerative Markov chain Monte Carlo with adaptation.
Bernoulli, 9 (3), .
Abstract
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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Published date: 2003
Keywords:
adaptive method, Bayesian inference, independence sampler, Metropolis–Hastings algorithm, regeneration
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Statistics
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Local EPrints ID: 30042
URI: http://eprints.soton.ac.uk/id/eprint/30042
ISSN: 1350-7265
PURE UUID: 40465834-e7f7-4bb4-99ef-c793ab720742
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Date deposited: 12 May 2006
Last modified: 16 Mar 2024 03:15
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Author:
Anatoly A. Zhigljavsky
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