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Importance Sampling for Continuous Time Markov Chains and Applications to Fluid Models

Author

Listed:
  • Paolo Baldi

    (Universita` di Roma Tor Vergata and Universita` di L'Aquila)

  • Mauro Piccioni

    (Universita` di Roma Tor Vergata and Universita` di L'Aquila)

Abstract

In this paper we determine the asymptotically efficient change of intensity for some problems of Monte Carlo simulation involving a finite state continuous time Markov process. Firstly, the computation of probabilities of large deviations of empirical averages from their asymptotic mean; second, the computation of probabilities of crossing a large level for the corresponding additive process. We are motivated by the study of overflows in a buffer whose input is modeled as a Markov fluid.

Suggested Citation

  • Paolo Baldi & Mauro Piccioni, 1999. "Importance Sampling for Continuous Time Markov Chains and Applications to Fluid Models," Methodology and Computing in Applied Probability, Springer, vol. 1(4), pages 375-390, December.
    • Handle: RePEc:spr:metcap:v:1:y:1999:i:4:d:10.1023_a:1010050800089
    DOI: 10.1023/A:1010050800089
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    Cited by:

    1. Macci, Claudio, 2009. "Convergence of large deviation rates based on a link between wave governed random motions and ruin processes," Statistics & Probability Letters, Elsevier, vol. 79(2), pages 255-263, January.

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