ACM Home Page
Please provide us with feedback. Feedback
Adaptive monte carlo methods for rare event simulation: adaptive monte carlo methods for rare event simulations
Full text PdfPdf (162 KB)
Source Winter Simulation Conference archive
Proceedings of the 34th conference on Winter simulation: exploring new frontiers table of contents
San Diego, California
SESSION: Advanced tutorials table of contents
Pages: 108 - 115  
Year of Publication: 2002
ISBN:0-7803-7615-3
Author
Ming-hua Hsieh  National Chengchi University, Wenshan, Taiwan
Sponsors
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
ACM: Association for Computing Machinery
(SCS) : The Society for Modeling and Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
Publisher
Winter Simulation Conference 
Bibliometrics
Downloads (6 Weeks): 23,   Downloads (12 Months): 148,   Citation Count: 0
Additional Information:

abstract   references   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

We review two types of adaptive Monte Carlo methods for rare event simulations. These methods are based on importance sampling. The first approach selects importance sampling distributions by minimizing the variance of importance sampling estimator. The second approach selects importance sampling distributions by minimizing the cross entropy to the optimal importance sampling distribution. We also review the basic concepts of importance sampling in the rare event simulation context. To make the basic concepts concrete, we introduce these ideas via the study of rare events of M/M/1 queues.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Al-Qaq, W. A., M. Devetsikiotis, and J. K. Townsend. 1995. Stochastic gradient optimization of importance sampling for the efficient simulation of digital communication systems. IEEE Transactions on Communications, 43:2975--2985.
 
2
 
3
Bucklew, J. 1990. Large Deviation Techniques in Decision, Simulation, and Estimation. John Wiley and Sons, Inc., New York.
 
4
 
5
Dembo, A. and O. Zeitouni. 1993. Large Deviation Techniques and Applications. Jones and Bartlett, Boston.
 
6
Devetsikiotis, M. and J. K. Townsend. 1993a. An algorithmic approach to the optimization of importance sampling parameters in digital communication system simulation. IEEE Transactions on Communications 41:1464--1473.
 
7
 
8
Fu, M. C., and J. Q. Hu. 1997. Conditional Monte Carlo: Gradient estimation and optimization applications. Kluwer Academic Publisher, Boston.
 
9
Glasserman, P. 1991. Gradient estimation via perturbation analysis. Boston: Kluwer.
10
11
 
12
Glynn, P. W. 1994. Efficiency improvement techniques. Annals of Oper. Res., 53:175--197.
 
13
 
14
Hammersley, J. M. and D. C. Handscomb. 1965. Monte Carlo Methods. Methuen & Co. Ltd., London.
15
 
16
Lehtonen, T. and H. Nyrhinen. 1992. Simulating level-crossing probabilities by importance sampling. Adv. in Appl. Probab., 24(4):858--874.
 
17
Lieber, D., R. Y. Rubinstein and D. Elmakis. 1997. Quick estimation of rare events in stochastic networks. IEEE Trans. Rel., 46(2):254--265.
 
18
 
19
Kapur, J. N. and H. K. Kesavan. 1992. Entropy Optimization Principles with Applications. Academic Press.
 
20
Karlin, S. and H. M. Taylor. 1975. A First Course in Stochastic Processes. Academic Press, New York-London, second edition.
 
21
Kiefer, J., and J. Wolfowitz. 1952. Stochastic estimation of the maximum of a regression function. Annals of Mathematical Statistics, 23:462--466.
 
22
Robbins, H., and S. Monro. 1951. A stochastic approximation method. Annals of Mathematical Statistics, 22:400--407.
 
23
 
24
Rubinstein, R. Y. 1997. Optimization of computer simulation models with rare events. European Journal of Operations Research 99:89--112.
 
25
Rubinstein, R. Y. 1999. Rare event simulation via crossentropy and importance sampling. In Second International Workshop on Rare Event Simulation, RESIM 99, 1--17.
 
26
Rubinstein, R. Y. and B. Melamed. 1998. Modern Simulation and Modeling. Wiley.
 
27
Siegmund, D. 1976. Importance sampling in the Monte Carlo study of sequential tests. Ann. Statist., 4(4):673--684.
 
28
 
29