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Regenerative Markov Chain Monte Carlo for Any Distribution

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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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Keywords: Markov chain Monte Carlo; Primary 65C05; Regenerative; Secondary 78M31, 80M31; Simulation

Document Type: Research Article

Affiliations: 1: Department of ISDS,California State University, Fullerton,California, USA 2: Biosciences Division, Argonne National Laboratory, Argonne,Illinois, USA 3: Department of Mathematics,California State University, Fullerton,California, USA

Publication date: October 1, 2012

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