Simulation mathematics and random number generation

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Simulation mathematics and random number generation: mathematics for simulation

Full text

Pdf (204 KB)

Source

Winter Simulation Conference archive
Proceedings of the 33nd conference on Winter simulation table of contents

Arlington, Virginia

TUTORIAL SESSION: Advanced tutorials table of contents

Pages: 83 - 94

Year of Publication: 2001

ISBN:0-7803-7309-X

Author

Shane G. Henderson

Cornell University, Ithaca, NY

Sponsors

INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
NIST : National Institute of Standards and Technology
ACM: Association for Computing Machinery
SCS : The Society for Computer Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association

Publisher

IEEE Computer Society Washington, DC, USA

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 24, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues peer to peer

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

ABSTRACT

I survey several mathematical techniques and results that are useful in the context of stochastic simulation. The concepts are introduced through the study of a simple model of ambulance operation to ensure clarity, concreteness and cohesion.

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	Avramidis, A. N., J. R. Wilson. 1996. Integrated variance reduction strategies for simulation. Operations Research 44: 327-346.
	2	Billingsley, P. 1986. Probability and Measure, 2nd ed. Wiley, New York.
	3	Paul Bratley , Bennett L. Fox , Linus E. Schrage, A guide to simulation (2nd ed.), Springer-Verlag New York, Inc., New York, NY, 1987
	4	Paul Glasserman, Filtered Monte Carlo, Mathematics of Operations Research, v.18 n.3, p.610-634, Aug. 1993 [doi>10.1287/moor.18.3.610]
	5	Peter W. Glynn , Philip Heidelberger, Bias properties of budget constrained simulations, Operations Research, v.38 n.5, p.801-814, Sep./Oct. 1990 [doi>10.1287/opre.38.5.801]
	6	P. W. Glynn , D. L. Iglehart, Simulation output analysis using standardized time series, Mathematics of Operations Research, v.15 n.1, p.1-16, Feb. 1990 [doi>10.1287/moor.15.1.1]
	7	Peter W. Glynn, Some new results on the initial transient problem, Proceedings of the 27th conference on Winter simulation, p.165-170, December 03-06, 1995, Arlington, Virginia, United States [doi>10.1145/224401.224458]
	8	Shane G. Henderson, Mathematics and hybrid modeling: mathematics for simulation, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
	9	Shane G. Henderson , Peter W. Glynn, Computing Densities for Markov Chains via Simulation, Mathematics of Operations Research, v.26 n.2, p.375-400, May 2001 [doi>10.1287/moor.26.2.375.10562]
	10	Averill M. Law , David M. Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1999
	11	Meyn, S. P. and R. L. Tweedie. 1993. Markov Chains and Stochastic Stability. Springer-Verlag, New York.
	12	Prakasa Rao, B. L. S. 1983. Nonparametric Functional Estimation. Academic Press.
	13	Rice, J. A. 1988. Mathematical Statistics and Data Analysis. Wadsworth and Brooks/Cole, Pacific Grove, California.
	14	Serfling, R. J. 1980 Approximation Theorems of Mathematical Statistics.