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Adaptive Importance Sampling Technique for Markov Chains Using Stochastic Approximation

Author

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  • T. P. I. Ahamed

    (Department of Electrical Engineering, T. K. M. College of Engineering, Kollam 691005, India)

  • V. S. Borkar

    (School of Technology and Computer Science, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400005, India)

  • S. Juneja

    (School of Technology and Computer Science, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400005, India)

Abstract

For a discrete-time finite-state Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This scheme updates the change of measure at every transition using constant or decreasing step-size stochastic approximation. The updates are shown to concentrate asymptotically in a neighborhood of the desired zero-variance estimator. Through simulation experiments on simple Markovian queues, we observe that the proposed technique performs very well in estimating performance measures related to rare events associated with queue lengths exceeding prescribed thresholds. We include performance comparisons of the proposed algorithm with existing adaptive importance sampling algorithms on some examples. We also discuss the extension of the technique to estimate the infinite horizon expected discounted cost and the expected average cost.

Suggested Citation

  • T. P. I. Ahamed & V. S. Borkar & S. Juneja, 2006. "Adaptive Importance Sampling Technique for Markov Chains Using Stochastic Approximation," Operations Research, INFORMS, vol. 54(3), pages 489-504, June.
    • Handle: RePEc:inm:oropre:v:54:y:2006:i:3:p:489-504
    DOI: 10.1287/opre.1060.0291
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    References listed on IDEAS

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    1. Peter W. Glynn & Donald L. Iglehart, 1993. "Notes: Conditions for the Applicability of the Regenerative Method," Management Science, INFORMS, vol. 39(9), pages 1108-1111, September.
    2. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    3. Sigrún Andradóttir & Daniel P. Heyman & Teunis J. Ott, 1995. "On the Choice of Alternative Measures in Importance Sampling with Markov Chains," Operations Research, INFORMS, vol. 43(3), pages 509-519, June.
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    Cited by:

    1. Hernan P. Awad & Peter W. Glynn & Reuven Y. Rubinstein, 2013. "Zero-Variance Importance Sampling Estimators for Markov Process Expectations," Mathematics of Operations Research, INFORMS, vol. 38(2), pages 358-388, May.
    2. Pierre L’Ecuyer & Bruno Tuffin, 2011. "Approximating zero-variance importance sampling in a reliability setting," Annals of Operations Research, Springer, vol. 189(1), pages 277-297, September.
    3. Vivek Borkar & Jerzy Filar, 2013. "Markov chains, Hamiltonian cycles and volumes of convex bodies," Journal of Global Optimization, Springer, vol. 55(3), pages 633-639, March.
    4. Bahar Kaynar & Ad Ridder, 2009. "The Cross-Entropy Method with Patching for Rare-Event Simulation of Large Markov Chains," Tinbergen Institute Discussion Papers 09-084/4, Tinbergen Institute.
    5. Kaynar, Bahar & Ridder, Ad, 2010. "The cross-entropy method with patching for rare-event simulation of large Markov chains," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1380-1397, December.

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