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Importance Sampling in Monte Carlo Analyses

Author

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  • Charles E. Clark

    (System Development Corporation, Santa Monica, California)

Abstract

Some Monte Carlo analyses require hundreds of hours of high speed computer time. Many problems of current interest can not be handled because the computer time required would be too great. Statistical sampling procedures have been developed that greatly reduce the required computer time. Importance sampling is one of these. This paper is an elementary description of importance sampling as used in Monte Carlo analyses.

Suggested Citation

  • Charles E. Clark, 1961. "Importance Sampling in Monte Carlo Analyses," Operations Research, INFORMS, vol. 9(5), pages 603-620, October.
    • Handle: RePEc:inm:oropre:v:9:y:1961:i:5:p:603-620
    DOI: 10.1287/opre.9.5.603
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    Cited by:

    1. Tamiti Kenza & Ourbih-Tari Megdouda & Aloui Abdelouhab & Idjis Khelidja, 2018. "The use of variance reduction, relative error and bias in testing the performance of M/G/1 retrial queues estimators in Monte Carlo simulation," Monte Carlo Methods and Applications, De Gruyter, vol. 24(3), pages 165-178, September.
    2. Yanqing Wang & Liang Zhang & Shaoran Ren & Bo Ren & Bailian Chen & Jun Lu, 2020. "Identification of potential CO2 leakage pathways and mechanisms in oil reservoirs using fault tree analysis," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(2), pages 331-346, April.
    3. Matteo Fischetti & Domenico Salvagnin & Arrigo Zanette, 2009. "Fast Approaches to Improve the Robustness of a Railway Timetable," Transportation Science, INFORMS, vol. 43(3), pages 321-335, August.

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