While Markov chain Monte Carlo (MCMC) methods are frequently used for difficult calculations in a wide range of scientific disciplines, they suffer from a serious limitation: their samples are not independent and identically distributed. Consequently, estimates of expectations are biased
if the initial value of the chain is not drawn from the target distribution. Regenerative simulation provides an elegant solution to this problem. In this article, we propose a simple regenerative MCMC algorithm to generate variates for any distribution.
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Markov chain Monte Carlo;
Secondary 78M31, 80M31;
Document Type: Research Article
Department of ISDS,California State University, Fullerton,California, USA
Biosciences Division, Argonne National Laboratory, Argonne,Illinois, USA
Department of Mathematics,California State University, Fullerton,California, USA
Publication date: October 1, 2012