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The score function approach for sensitivity analysis of computer simulation models

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  • Rubinstein, Reuven Y.

Abstract

Some theoretical and practical aspect of the score function (SF) approach for estimating the sensitivities of computer simulation models and solving the so-called “what if” problem (performance extrapolation) are considered. It is shown that both the sensitivities (gradients, Hessians, etc.) and the performance extrapolation can be derived simultaneously by simulating only a single sample path from the nominal system. It is also shown that the SF approach can be efficiently applied for DESS (discrete event static systems, example: reliability models and stochastic networks) and for DEDS (discrete events dynamic systems, example: queuing networks) under light traffics. Control variates procedure for variance reduction is presented as well

Suggested Citation

  • Rubinstein, Reuven Y., 1986. "The score function approach for sensitivity analysis of computer simulation models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 28(5), pages 351-379.
    • Handle: RePEc:eee:matcom:v:28:y:1986:i:5:p:351-379
    DOI: 10.1016/0378-4754(86)90072-8
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    References listed on IDEAS

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    1. Reuven Y. Rubinstein & Ruth Marcus, 1985. "Efficiency of Multivariate Control Variates in Monte Carlo Simulation," Operations Research, INFORMS, vol. 33(3), pages 661-677, June.
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    1. Marie Chiron & Jérôme Morio & Sylvain Dubreuil, 2023. "Local Sensitivity of Failure Probability through Polynomial Regression and Importance Sampling," Mathematics, MDPI, vol. 11(20), pages 1-19, October.
    2. Schweinberger, Michael & Snijders, Tom A.B., 2007. "Markov models for digraph panel data: Monte Carlo-based derivative estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4465-4483, May.
    3. Marvin K. Nakayama & Perwez Shahabuddin, 1998. "Likelihood Ratio Derivative Estimation for Finite-Time Performance Measures in Generalized Semi-Markov Processes," Management Science, INFORMS, vol. 44(10), pages 1426-1441, October.
    4. Ho, Yu-Chi & Li, Shu & Vakili, Pirooz, 1988. "On the efficient generation of discrete event sample paths under different system parameter values," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 30(4), pages 347-370.
    5. Chabridon, Vincent & Balesdent, Mathieu & Bourinet, Jean-Marc & Morio, Jérôme & Gayton, Nicolas, 2018. "Reliability-based sensitivity estimators of rare event probability in the presence of distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 164-178.
    6. Rubinstein, Reuven Y. & Shapiro, Alexander, 1990. "Optimization of static simulation models by the score function method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(4), pages 373-392.
    7. Li, Jinghui & Mosleh, Ali & Kang, Rui, 2011. "Likelihood ratio gradient estimation for dynamic reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1667-1679.
    8. Yijie Peng & Li Xiao & Bernd Heidergott & L. Jeff Hong & Henry Lam, 2022. "A New Likelihood Ratio Method for Training Artificial Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 638-655, January.
    9. Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    11. Rubinstein, Reuven Y., 1991. "Modified importance sampling for performance evaluation and sensitivity analysis of computer simulation models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 33(1), pages 1-22.
    12. Laub, Patrick J. & Salomone, Robert & Botev, Zdravko I., 2019. "Monte Carlo estimation of the density of the sum of dependent random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 23-31.
    13. Jingxu Xu & Zeyu Zheng, 2023. "Gradient-Based Simulation Optimization Algorithms via Multi-Resolution System Approximations," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 633-651, May.

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