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Input modeling: input model uncertainty: why do we care and what should we do about it?
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Proceedings of the 35th conference on Winter simulation: driving innovation table of contents
New Orleans, Louisiana
SESSION: Advanced tutorials table of contents
Pages: 90 - 100  
Year of Publication: 2003
ISBN:0-7803-8132-7
Author
Shane G. Henderson  Cornell Universtiy, Ithaca, NY
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
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
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
Publisher
Winter Simulation Conference 
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ABSTRACT

An input model is a collection of distributions together with any associated parameters that are used as primitive inputs in a simulation model. Input model uncertainty arises when one is not completely certain what distributions and/or parameters to use. This tutorial attempts to provide a sense of why one should consider input uncertainty and what methods can be used to deal with it.


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
Andradóttir, S., and P. W. Glynn. 2003. Computing Bayesian means using simulation. Submitted for publication.
 
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3
 
4
 
5
 
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Ben-Tal, A., and A. Nemirovski. 2000. Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming, Series A 88:411--424.
 
7
 
8
Cheng, R. C. H. 2000. Analysis of simulation output by resampling. International Journal of Simulation: Systems, Science & Technology 1:51--58.
 
9
 
10
Cheng, R. C. H., and W. Holland. 1997. Sensitivity of computer simulation experiments to errors in input data. Journal of Statistical Computation and Simulation 57:219--241.
 
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Cheng, R. C. H., and W. Holland. 1998. Two-point methods for assessing variability in simulation output. Journal of Statistical Computation and Simulation 60:183--205.
 
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Cheng, R. C. H., and W. Holland. 2003. Calculation of confidence intervals for simulation output. Submitted for publication.
 
13
14
 
15
 
16
 
17
 
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Draper, D. 1995. Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society. Series B 57:45--97.
 
19
 
20
 
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Helton, J. C. 1997. Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty. Journal of Statistical Computation and Simulation 57:3--76.
 
22
Helton, J. C., and F. J. Davis. 2003. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety 81:23--69.
 
23
 
24
Henderson, S. G. 2001. Mathematics for simulation. In Proceedings of the 2001 Winter Simulation Conference, ed. B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, 83--94. Piscataway NJ: IEEE.
 
25
Kleijnen, J. P. C. 1994. Sensitivity analysis versus uncertainty analysis: when to use what? In Predictability and Nonlinear Modelling in Natural Sciences and Economics, ed. J. Grasman and G. van Straten. Dordrecht: Kluwer Academic.
 
26
 
27
Kleijnen, J. P. C. 1998. Experimental design for sensitivity analysis, optimization, and validation of simulation models. In Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, ed. J. Banks. New York: Wiley.
 
28
 
29
30
 
31
 
32
Oberkampf, W. L., J. C. Helton, C. A. Joslyn, S. F. Wojtkiewicz, and S. Ferson. 2003. Challenge problems: uncertainty in system response given uncertain parameters. Available online via <http://www.sandia.gov/epistemic/eup_challenge.htm> {accessed July 6, 2003}.
 
33
 
34
 
35
 
36
Zouaoui, F., and J. R. Wilson. 2003a. Accounting for input model and parameter uncertainty in simulation. Submitted for publication.
 
37
Zouaoui, F., and J. R. Wilson. 2003b. Accounting for parameter uncertainty in simulation input modeling. IIE Transactions. To appear.