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WSC
2008
13 years 7 months ago
Selecting the best linear simulation metamodel
We consider the output of a simulation model of a system about which little is initially known. This output is often dependent on a large number of factors. It is helpful, in exam...
Russell Cheng
WSC
2000
13 years 6 months ago
Model abstraction for discrete event systems using neural networks and sensitivity information
STRACTION FOR DISCRETE EVENT SYSTEMS USING NEURAL NETWORKS AND SENSITIVITY INFORMATION Christos G. Panayiotou Christos G. Cassandras Department of Manufacturing Engineering Boston ...
Christos G. Panayiotou, Christos G. Cassandras, We...
WSC
2008
13 years 7 months ago
Stochastic kriging for simulation metamodeling
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide fl...
Bruce E. Ankenman, Barry L. Nelson, Jeremy Staum
WSC
2007
13 years 7 months ago
Regression models and experimental designs: a tutorial for simulation analysts
This tutorial explains the basics of linear regression metamodels—especially low-order polynomials—and the corresponding statistical designs—namely, fractional factorial des...
Jack P. C. Kleijnen
WSC
1998
13 years 6 months ago
Bayesian Model Selection when the Number of Components is Unknown
In simulation modeling and analysis, there are two situations where there is uncertainty about the number of parameters needed to specify a model. The first is in input modeling w...
Russell C. H. Cheng