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A Martingale Approach to Regenerative Simulation*

Published online by Cambridge University Press:  27 July 2009

Peter W. Glynn
Affiliation:
Department of Operations Research, Stanford University, Stanford, California 94305
Donald L. Iglehart
Affiliation:
Department of Operations Research, Stanford University, Stanford, California 94305

Abstract

The standard regenerative method for estimating steady-state parameters is extended to permit cycles that begin and end in different states. This result is established using the Dynkin martingale and a related solution to Poisson's equation. We compare the variance constant that appears in the associated central limit theorem with that arising from cycles that begin and end in the same state. The standard regenerative method has a smaller variance constant than does the alternative.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1995

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References

1.Glynn, P.W. (1984). Some asymptotic formulas for Markov chains with applications to simulation. Journal of Statistical Computer Simulation 19: 97112.CrossRefGoogle Scholar
2.Iglehart, D.L. (1978). The regenerative method for simulation analysis. In Chandy, K.M. & Yeh, R.T. (eds.). Current trends in programming methodology. Vol. III: Software modeling. Englewood Cliffs, NJ: Prentice-Hall, chapter 2, pp. 5271.Google Scholar
3.Karlin, S. & Taylor, H.M. (1981). A second course in stochastic processes. New York: Academic Press.Google Scholar