|
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.
|
| |
2
|
Russell R. Barton , Stephen E. Chick , Russell C. H. Cheng , Shane G. Henderson , Averill M. Law , Bruce W. Schmeiser , Lawrence M. Leemis , Lee W. Schruben , James R. Wilson, Panel discussion on current issues in input modeling: panel on current issues in simulation input modeling, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
|
 |
3
|
|
| |
4
|
|
| |
5
|
|
| |
6
|
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.
|
| |
11
|
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.
|
| |
12
|
Cheng, R. C. H., and W. Holland. 2003. Calculation of confidence intervals for simulation output. Submitted for publication.
|
| |
13
|
|
 |
14
|
|
| |
15
|
|
| |
16
|
|
| |
17
|
|
| |
18
|
Draper, D. 1995. Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society. Series B 57:45--97.
|
| |
19
|
|
| |
20
|
|
| |
21
|
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.
|
|