Regenerative Markov Chain Monte Carlo for any distribution.

Journal Article · Sun Jan 01 00:00:00 EST 2012 · Communications in Statistics, Part B: Simulation and Computation

DOI:https://doi.org/10.1080/03610918.2011.615433· OSTI ID:1047434

Minh, D ^[1]

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1. Biosciences Division

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

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: DE-AC02-06CH11357
• OSTI ID: 1047434
• Report Number(s): ANL/BIO/JA-66161; TRN: US201216%%282
• Journal Information: Communications in Statistics, Part B: Simulation and Computation, Vol. 41, Issue 9; ISSN 0361-0918
• Country of Publication: United States
• Language: ENGLISH

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ALGORITHMS
DISTRIBUTION
SIMULATION
TARGETS
MONTE CARLO METHOD