Bernoulli

Self-regenerative Markov chain Monte Carlo with adaptation

Sujit K. Sahu and Anatoly A. Zhigljavsky

Full-text: Open access

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.

Article information

Source
Bernoulli Volume 9, Number 3 (2003), 395-422.

Dates
First available in Project Euclid: 6 October 2003

Permanent link to this document
http://projecteuclid.org/euclid.bj/1065444811

Digital Object Identifier
doi:10.3150/bj/1065444811

Mathematical Reviews number (MathSciNet)
MR1997490

Zentralblatt MATH identifier
02075975

Citation

Sahu, Sujit K.; Zhigljavsky, Anatoly A. Self-regenerative Markov chain Monte Carlo with adaptation. Bernoulli 9 (2003), no. 3, 395--422. doi:10.3150/bj/1065444811. http://projecteuclid.org/euclid.bj/1065444811.


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