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{1065444811,
author = {Sahu, Sujit K. and Zhigljavsky, Anatoly A.},
title = {Self-regenerative Markov chain Monte Carlo with adaptation},
journal = {Bernoulli},
volume = {9},
number = {3},
year = {2003},
pages = { 395-422},
language = {en},
url = {http://dml.mathdoc.fr/item/1065444811}
}
Sahu, Sujit K.; Zhigljavsky, Anatoly A. Self-regenerative Markov chain Monte Carlo with adaptation. Bernoulli, Tome 9 (2003) no. 3, pp. 395-422. http://gdmltest.u-ga.fr/item/1065444811/