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Comparing stochastic systems using regenerative simulation with common random numbers

Published online by Cambridge University Press:  01 July 2016

Philip Heidelberger  and
Donald L. Iglehart
Philip Heidelberger*
Affiliation:
IBM Thomas J. Watson Research Center
Donald L. Iglehart*
Affiliation:
Stanford University
*
∗ Postal address: IBM Thomas J. Watson Research Center, P.O. Box 218, Yorktown Heights, N.Y. 10598, U.S.A.
∗∗ Postal address: Department of Operations Research, Stanford University, Stanford, CA 94305, U.S.A.
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Abstract

Suppose two alternative designs for a stochastic system are to be compared. These two systems can be simulated independently or dependently. This paper presents a method for comparing two regenerative stochastic processes in a dependent fashion using common random numbers. A set of sufficient conditions is given that guarantees that the dependent simulations will produce a variance reduction over independent simulations. Numerical examples for a variety of simple stochastic models are included which illustrate the variance reduction achieved.

Keywords

CENTRAL LIMIT THEOREMCOMMON RANDOM NUMBERSREGENERATIVE METHODSIMULATIONSTATISTICAL ESTIMATIONVARIANCE REDUCTION

Information

Type
Research Article
Information
Advances in Applied Probability , Volume 11 , Issue 4 , December 1979 , pp. 804 - 819
Copyright
Copyright © Applied Probability Trust 1979 

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Footnotes

Research supported by NSF Grant MCS-23607 and ONR Contract N00014-76-C-0578.

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