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The Move-to-Front Rule: A Case Study for two Perfect Sampling Algorithms

Published online by Cambridge University Press:  27 July 2009

James Allen Fill
Affiliation:
Department of Mathematical Sciences, The Johns Hopkins University, Baltimore, Maryland 21218–2682

Abstract

The elementary problem of exhaustively sampling a finite population without replacement is used as a nonreversible test case for comparing two recently proposed MCMC algorithms for perfect sampling, one based on backward coupling and the other on strong stationary duality. The backward coupling algorithm runs faster in this case, but the duality-based algorithm is unbiased for user impatience. An interesting by-product of the analysis is a new and simple stochastic interpretation of a mixing-time result for the move-to-front rule.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1998

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