@article {Minh:2012:0361-0918:1745,
title = "Regenerative Markov Chain Monte Carlo for Any Distribution",
journal = "Communications in Statistics: Simulation and Computation",
parent_itemid = "infobike://tandf/lssp",
publishercode ="tandf",
year = "2012",
volume = "41",
number = "9",
publication date ="2012-10-01T00:00:00",
pages = "1745-1760",
itemtype = "ARTICLE",
issn = "0361-0918",
eissn = "1532-4141",
url = "https://www.ingentaconnect.com/content/tandf/lssp/2012/00000041/00000009/art00016",
doi = "doi:10.1080/03610918.2011.615433",
keyword = "Regenerative, Primary 65C05, Secondary 78M31, 80M31, Simulation, Markov chain Monte Carlo",
author = "Minh, Do Le (Paul) and Minh, David D. L. and Nguyen, Andrew L.",
abstract = "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.",
}