Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Driving frequency selection for frequency domain simulation experiments
Operations Research
Simulation factor screening using harmonic analysis
Management Science
WSC '94 Proceedings of the 26th conference on Winter simulation
Proceedings of the 30th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Reducing input parameter uncertainty for simulations
Proceedings of the 33nd conference on Winter simulation
Nested Partitions Method for Global Optimization
Operations Research
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
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
A simple model for assessing output uncertainty in stochastic simulation systems
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
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One goal in simulation experimentation is to identify which input parameters most significantly influence the mean of simulation output. Another goal is to obtain good parameter estimates for a response model that quantifies how the mean output depends on influential input parameters. The majority of experimental design techniques focus on either one goal or the other. This paper uses a design criterion for follow-up experiments that jointly identifies the important parameters and reduces the variance of parameter estimates. The criterion is entropy-based, and is applied to a critical care facility simulation.