Mathematics and hybrid modeling

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Mathematics and hybrid modeling: mathematics for simulation

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Winter Simulation Conference archive
Proceedings of the 32nd conference on Winter simulation table of contents

Orlando, Florida

TUTORIAL SESSION: Advanced tutorials table of contents

Pages: 137 - 146

Year of Publication: 2000

ISBN:0-7803-6582-8

Author

Shane G. Henderson

University of Michigan, Ann Arbor, MI

Sponsors

IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
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
SIGSIM: ACM Special Interest Group on Simulation and Modeling
SCS : The Society for Computer Simulation International

Publisher

Society for Computer Simulation International San Diego, CA, USA

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 8, Citation Count: 2

Additional Information:

abstract references cited by collaborative colleagues

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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	Billingsley, P. 1986. Probability and measure. 2d ed. New York: Wiley.
	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	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]
	4	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]
	5	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]
	6	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]
	7	Henderson, S. G. and P. W. Glynn. 1999. Computing densities for Markov chains via simulation. Submitted for publication.
	8	Averill M. Law , David M. Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1999
	9	Meyn, S. P. and R. L. Tweedie. 1993. Markov chains and stochastic stability. New York: Springer-Verlag.
	10	Rice, J. A. 1988. Mathematical statistics and data analysis. Pacific Grove, California: Wadsworth and Brooks/Cole.
	11	Serfling, R. J. 1980 Approximation theorems of mathematical statistics. New York: Wiley.

CITED BY 2

	Shane G. Henderson, Simulation mathematics and random number generation: mathematics for simulation, Proceedings of the 33nd conference on Winter simulation, December 09-12, 2001, Arlington, Virginia
	Shane G. Henderson, Input modeling: input model uncertainty: why do we care and what should we do about it?, Proceedings of the 35th conference on Winter simulation: driving innovation, December 07-10, 2003, New Orleans, Louisiana

Collaborative Colleagues:

Shane G. Henderson: colleagues

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