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Splitting for rare-event simulation

Published: 03 December 2006 Publication History

Abstract

Splitting and importance sampling are the two primary techniques to make important rare events happen more frequently in a simulation, and obtain an unbiased estimator with much smaller variance than the standard Monte Carlo estimator. Importance sampling has been discussed and studied in several articles presented at the Winter Simulation Conference in the past. A smaller number of WSC articles have examined splitting. In this paper, we review the splitting technique and discuss some of its strengths and limitations from the practical viewpoint. We also introduce improvements in the implementation of the multilevel splitting technique. This is done in a setting where we want to estimate the probability of reaching B before reaching (or returning to) A when starting from a fixed state x0B, where A and B are two disjoint subsets of the state space and B is very rarely attained. This problem has several practical applications.

References

[1]
Akin, O., and J. K. Townsend. 2001. Efficient simulation of TCP/IP networks characterized by non-rare events using DPR-based splitting. In Proceedings of IEEE Globecom, 1734--1740.
[2]
Blom, H. A. P., G. J. Bakker, J. Krystul, M. H. C. Everdij, B. K. Obbink, and M. B. Klompstra. 2005. Sequential Monte Carlo simulation of collision risk in free flight air traffic. Technical report, Project HYBRIDGE IST-2001-32460.
[3]
Booth, T. E. 1982. Automatic importance estimation in forward Monte Carlo calculations. Transactions of the American Nuclear Society 41:308--309.
[4]
Booth, T. E. 1985. Monte Carlo variance comparison for expected-value versus sampled splitting. Nuclear Science and Engineering 89:305--309.
[5]
Booth, T. E., and J. S. Hendricks. 1984. Importance estimation in forward Monte Carlo estimation. Nuclear Technology/Fusion 5:90--100.
[6]
Booth, T. E., and S. P. Pederson. 1992. Unbiased combinations of nonanalog Monte Carlo techniques and fair games. Nuclear Science and Engineering 110:254--261.
[7]
Bucklew, J. A. 2004. Introduction to rare event simulation. New York: Springer-Verlag.
[8]
Cancela, H., G. Rubino, and B. Tuffin. 2002. MTTF estimation by Monte Carlo methods using Markov models. Monte Carlo Methods and Applications 8 (4): 312--341.
[9]
Cérou, F., and A. Guyader. 2005, October. Adaptive multilevel splitting for rare event analysis. Technical Report 5710, INRIA.
[10]
Cérou, F., F. LeGland, P. Del Moral, and P. Lezaud. 2005. Limit theorems for the multilevel splitting algorithm in the simulation of rare events. In Proceedings of the 2005 Winter Simulation Conference, ed. F. B. A. M. E. Kuhl, N. M. Steiger and J. A. Joines, 682--691.
[11]
Del Moral, P. 2004. Feynman-Kac formulae. genealogical and interacting particle systems with applications. Probability and its Applications. New York: Springer.
[12]
Ermakov, S. M., and V. B. Melas. 1995. Design and analysis of simulation experiments. Dordrecht, The Netherlands: Kluwer Academic.
[13]
Fox, B. L. 1999. Strategies for quasi-Monte Carlo. Boston, MA: Kluwer Academic.
[14]
Garvels, M. J. J. 2000. The splitting method in rare event simulation. Ph.D. thesis, Faculty of mathematical Science, University of Twente, The Netherlands.
[15]
Garvels, M. J. J., and D. P. Kroese. 1998. A comparison of RESTART implementations. In Proceedings of the 1998 Winter Simulation Conference, 601--609: IEEE Press.
[16]
Garvels, M. J. J., D. P. Kroese, and J.-K.C.W. Van Ommeren. 2002. On the importance function in splitting simulation. European Transactions on Telecommunications 13 (4): 363--371.
[17]
Glasserman, P., P. Heidelberger, and P. Shahabuddin. 1999. Asymptotically optimal importance sampling and stratification for pricing path dependent options. Mathematical Finance 9 (2): 117--152.
[18]
Glasserman, P., P. Heidelberger, P. Shahabuddin, and T. Zajic. 1998. A large deviations perspective on the efficiency of multilevel splitting. IEEE Transactions on Automatic Control AC-43 (12): 1666--1679.
[19]
Glasserman, P., P. Heidelberger, P. Shahabuddin, and T. Zajic. 1999. Multilevel splitting for estimating rare event probabilities. Operations Research 47 (4): 585--600.
[20]
Glynn, P. W., and D. L. Iglehart. 1989. Importance sampling for stochastic simulations. Management Science 35:1367--1392.
[21]
Gorg, C., and O. Fuss. 1999. Simulating rare event details of atm delay time distributions with restart/Ire. In Proceedings of the IEE International Teletrafic Congress, ITC16, 777--786: Elsevier.
[22]
Hammersley, J. M., and D. C. Handscomb. 1964. Monte carlo methods. London: Methuen.
[23]
Harris, T. 1963. The theory of branching processes. New York: Springer-Verlag.
[24]
Heidelberger, P. 1995. Fast simulation of rare events in queueing and reliability models. ACM Transactions on Modeling and Computer Simulation 5 (1): 43--85.
[25]
Kahn, H., and T. E. Harris. 1951. Estimation of particle transmission by random sampling. National Bureau of Standards Applied Mathematical Series 12:27--30.
[26]
L'Ecuyer, P., V. Demers, and B. Tuffin. 2006. Rare-events, splitting, and quasi-Monte Carlo. ACM Transactions on Modeling and Computer Simulation. To appear.
[27]
L'Ecuyer, P., C. Lécot, and B. Tuffin. 2005. A randomized quasi-Monte Carlo simulation method for Markov chains. Submitted to Operations Research, under revision.
[28]
L'Ecuyer, P., and C. Lemieux. 2000. Variance reduction via lattice rules. Management Science 46 (9): 1214--1235.
[29]
Melas, V. B. 1997. On the efficiency of the splitting and roulette approach for sensitivity analysis. In Proceedings of the 1997 Winter Simulation Conference, 269--274. Piscataway, NJ: IEEE Press.
[30]
Owen, A. B. 1998. Latin supercube sampling for very high-dimensional simulations. ACM Transactions on Modeling and Computer Simulation 8 (1): 71--102.
[31]
Parekh, S., and J. Walrand. 1989. A quick simulation method for excessive backlogs in networks of queues. IEEE Transactions on Automatic Control AC-34:54--56.
[32]
Pederson, S. P., R. A. Forster, and T. E. Booth. 1997. Confidence intervals for Monte Carlo transport simulation. Nuclear Science and Engineering 127:54--77.
[33]
Shahabuddin, P. 1994. Importance sampling for the simulation of highly reliable markovian systems. Management Science 40 (3): 333--352.
[34]
Spanier, J., and E. M. Gelbard. 1969. Monte Carlo principles and neutron transport problems. Reading, Massachusetts: Addison-Wesley.
[35]
Taylor, H. M., and S. Karlin. 1998. An introduction to stochastic modeling. Third ed. San Diego: Academic Press.
[36]
Vasicek, O. 1977. An equilibrium characterization of the term structure. Journal of Financial Economics 5:177--188.
[37]
Villén-Altamirano, J. 2006. Rare event RESTART simulation of two-stage networks. European Journal of Operations Research. To appear.
[38]
Villén-Altamirano, M., and J. Villén-Altamirano. 1994. RESTART: A straightforward method for fast simulation of rare events. In Proceedings of the 1994 Winter Simulation Conference, 282--289: IEEE Press.
[39]
Villén-Altamirano, M., and J. Villén-Altamirano. 2002. Analysis of RESTART simulation: Theoretical basis and sensitivity study. European Transactions on Telecommunications 13 (4): 373--386.
[40]
Villén-Altamirano, M., and J. Villén-Altamirano. 2006. On the efficiency of RESTART for multidimensional systems. ACM Transactions on Modeling and Computer Simulation 16 (3): 251--279.

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cover image ACM Conferences
WSC '06: Proceedings of the 38th conference on Winter simulation
December 2006
2429 pages
ISBN:1424405017

Sponsors

  • IIE: Institute of Industrial Engineers
  • ASA: American Statistical Association
  • IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
  • IEEE-CS\DATC: The IEEE Computer Society
  • SIGSIM: ACM Special Interest Group on Simulation and Modeling
  • NIST: National Institute of Standards and Technology
  • (SCS): The Society for Modeling and Simulation International
  • INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation

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Winter Simulation Conference

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Published: 03 December 2006

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WSC06
Sponsor:
  • IIE
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  • IEICE ESS
  • IEEE-CS\DATC
  • SIGSIM
  • NIST
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  • INFORMS-CS
WSC06: Winter Simulation Conference 2006
December 3 - 6, 2006
California, Monterey

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WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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