Optimal importance sampling for quick simulation of highly reliable Markovian systems

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Optimal importance sampling for quick simulation of highly reliable Markovian systems

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Winter Simulation Conference archive
Proceedings of the 25th conference on Winter simulation table of contents

Los Angeles, California, United States

Pages: 437 - 444

Year of Publication: 1993

ISBN:0-7803-1381-X

Author

Stephen G. Strickland

Dept. of Systems Engineering, University of Virginia, Charlottesville, VA

Sponsors

IEEE-CS : Computer Society
IEEE-SMCS : Systems, Man & Cybernetics Society
ACM: Association for Computing Machinery
ORSA : Operations Research Society of America
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
SCS : Society for Computer Simulation
ASA : American Statistical Association
NIST : National Institue of Standards & Technology
TIMS/CSG :

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 15, Citation Count: 4

Additional Information:

references cited by collaborative colleagues

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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	Ravindra K. Ahuja , Thomas L. Magnanti , James B. Orlin, Network flows: theory, algorithms, and applications, Prentice-Hall, Inc., Upper Saddle River, NJ, 1993
	2	Paul Bratley , Bennett L. Fox , Linus E. Schrage, A guide to simulation (2nd ed.), Springer-Verlag New York, Inc., New York, NY, 1987
	3	Cottrell, M., Fort, J.C., and Malgouyres, G. 1983., "Large Deviations and Rare Events in the Study of Stochastic Algorithms," IEEE Trans. on Automatzc Control, AC-28, pp. 907-920.
	4	P W. Glynn , D. L. Iglehart, Importance sampling for stochastic simulations, Management Science, v.35 n.11, p.1367-1392, Nov. 1989 [doi>10.1287/mnsc.35.11.1367]
	5	Ambuj Goyal , Perwez Shahabuddin , Philip Heidelberger , Victor F. Nicola , Peter W. Glynn, A Unified Framework for Simulating Markovian Models of Highly Dependable Systems, IEEE Transactions on Computers, v.41 n.1, p.36-51, January 1992 [doi>10.1109/12.123381]
	6	I-Iammersley, J.M. and Handscomb, D.C. 1964. Monte Carlo Methods, Metheun, London.
	7	Hordijk, A., Iglehart, D.L., and Schassberger, R. 1976. "Discrete-Time Methods of Simulating Continuous-Time Markov Chains," Adv. in Applied Probability, 8, pp. 772-788.
	8	Nakayama, M. 1993. "A Characterization of the Simple Failure Biasing Method for Simulations of Highly Reliable Markovian Systems," submitted for publication.
	9	Parekh, S., and Walrand, j. 1989. "A Quick Simulation Method For Excessive Backlogs in Networks of Queues," IEEE Trans. on Automatic Control, AC-34, pp. 54-66.
	10	Shahabuddin, P. 1991. Importance Samphng For the Simulation of Highly Reliable Markovian Systems, Research Report RC 16729, IBM Research Division, T.J. Watson Research Center, Yorktown Heights, NY, submitted for publication.
	11	Walrand, J. 1988. An Inlroductzon to Queueing Networks, Prentice Hall, New York.

CITED BY 4

	Philip Heidelberger, Fast simulation of rare events in queueing and reliability models, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.5 n.1, p.43-85, Jan. 1995
	Indira Kuruganti , Stephen G. Strickland, Importance sampling for Markov chains: computing variance and determining optimal measures, Proceedings of the 28th conference on Winter simulation, p.273-280, December 08-11, 1996, Coronado, California, United States
	Marvin K. Nakayama, Fast simulation methods for highly dependable systems, Proceedings of the 26th conference on Winter simulation, p.221-228, December 11-14, 1994, Orlando, Florida, United States
	Marvin K. Nakayama , Perwez Shahabuddin, Quick Simulation Methods For Estimating The Unreliability Of Regenerative Models Of Large, Highly Reliable Systems, Probability in the Engineering and Informational Sciences, v.18 n.3, p.339-368, July 2004

Collaborative Colleagues:

Stephen G. Strickland: colleagues

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