A quasi-Monte Carlo Metropolis algorithm.

Proc Natl Acad Sci U S A

Department of Statistics, Stanford University, Stanford, CA 94305, USA.

Published: June 2005

This work presents a version of the Metropolis-Hastings algorithm using quasi-Monte Carlo inputs. We prove that the method yields consistent estimates in some problems with finite state spaces and completely uniformly distributed inputs. In some numerical examples, the proposed method is much more accurate than ordinary Metropolis-Hastings sampling.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1150275PMC
http://dx.doi.org/10.1073/pnas.0409596102DOI Listing

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