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Random Assignment Versus Fixed Assignment in Multilevel Importance Splitting for Estimating Stochastic Reach Probabilities

Author

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  • Hao Ma

    (Delft University of Technology
    Northwestern Polytechnical University)

  • Henk A. P. Blom

    (Delft University of Technology)

Abstract

This paper focuses on estimating reach probability of a closed unsafe set by a stochastic process. A well-developed approach is to make use of multi-level MC simulation, which consists of encapsulating the unsafe set by a sequence of increasing closed sets and conducting a sequence of MC simulations to estimate the reach probability of each inner set from the previous set. An essential step is to copy (split) particles that have reached the next level (inner set) prior to conducting a MC simulation to the next level. The aim of this paper is to prove that the variance of the multi-level MC estimated reach probability under fixed assignment splitting is smaller or equal than under random assignment splitting methods. The approaches are illustrated for a geometric Brownian motion example.

Suggested Citation

  • Hao Ma & Henk A. P. Blom, 2022. "Random Assignment Versus Fixed Assignment in Multilevel Importance Splitting for Estimating Stochastic Reach Probabilities," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 2313-2338, December.
    • Handle: RePEc:spr:metcap:v:24:y:2022:i:4:d:10.1007_s11009-021-09892-4
    DOI: 10.1007/s11009-021-09892-4
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    References listed on IDEAS

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    1. Reuven Rubinstein, 2010. "Randomized Algorithms with Splitting: Why the Classic Randomized Algorithms Do Not Work and How to Make them Work," Methodology and Computing in Applied Probability, Springer, vol. 12(1), pages 1-50, March.
    2. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin & Tim Zajic, 1999. "Multilevel Splitting for Estimating Rare Event Probabilities," Operations Research, INFORMS, vol. 47(4), pages 585-600, August.
    3. Zdravko I. Botev & Dirk P. Kroese, 2008. "An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting," Methodology and Computing in Applied Probability, Springer, vol. 10(4), pages 471-505, December.
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