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Potentially unlimited variance reduction in importance sampling of Markov chains

Part of:Combinatorial probability

Published online by Cambridge University Press:  01 July 2016

Sigrún Andradóttir,
Daniel P. Heyman and
Teunis J. Ott
Sigrún Andradóttir*
Affiliation:
University of Wisconsin-Madison
Daniel P. Heyman*
Affiliation:
Bellcore
Teunis J. Ott*
Affiliation:
Bellcore
*
* Postal address: Department of Industrial Engineering, University of Wisconsin-Madison, 1513 University Avenue, Madison, WI 53706, USA. (Current address: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.)
** Postal address: Bellcore, 445 South Street, Morristown, NJ 07962, USA.
** Postal address: Bellcore, 445 South Street, Morristown, NJ 07962, USA.
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Abstract

We consider the application of importance sampling in steady-state simulations of finite Markov chains. We show that, for a large class of performance measures, there is a choice of the alternative transition matrix for which the ratio of the variance of the importance sampling estimator to the variance of the naive simulation estimator converges to zero as the sample path length goes to infinity. Obtaining this ‘optimal’ transition matrix involves computing the performance measure of interest, so the optimal matrix cannot be computed in precisely those situations where simulation is required to estimate steady-state performance. However, our results show that alternative transition matrices of the form Q = P + E/T, where P is the original transition matrix and T is the sample path length, can be expected to provide good results. Moreover, we provide an iterative algorithm for obtaining alternative transition matrices of this form that converge to the optimal matrix as the number of iterations increases, and present an example that shows that spending some computer time iterating this algorithm and then conducting the simulation with the resulting alternative transition matrix may provide considerable variance reduction when compared to naive simulation.

Keywords

MONTE CARLO SIMULATIONPOISSON'S EQUATION

MSC classification

Primary:60C05: Combinatorial probability

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 28 , Issue 1 , March 1996, pp. 166 - 188
Copyright
Copyright © Applied Probability Trust 1996 

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