The Annals of Applied Probability

Perfect sampling of ergodic Harris chains

J. N. Corcoran and R. L. Tweedie

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We develop an algorithm for simulating “perfect” random samples from the invariant measure of a Harris recurrent Markov chain.The method uses backward coupling of embedded regeneration times and works most effectively for stochastically monotone chains, where paths may be sandwiched between “upper” and “lower” processes. We give an approach to finding analytic bounds on the backward coupling times in the stochastically monotone case. An application to storage models is given.

Article information

Ann. Appl. Probab., Volume 11, Number 2 (2001), 438-451.

First available in Project Euclid: 5 March 2002

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Zentralblatt MATH identifier

Primary: 60J10: Markov chains (discrete-time Markov processes on discrete state spaces) 60K05: Renewal theory 60K30: Applications (congestion, allocation, storage, traffic, etc.) [See also 90Bxx]

Irreducible Markov chains invariant measures geometric ergodicity backward coupling coupling from the past exact sampling perfect sampling queues storage models


Corcoran, J. N.; Tweedie, R. L. Perfect sampling of ergodic Harris chains. Ann. Appl. Probab. 11 (2001), no. 2, 438--451. doi:10.1214/aoap/1015345299.

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