Uniform and bootstrap resampling of empirical distributions
WSC '93 Proceedings of the 25th conference on Winter simulation
Bayesian analysis for simulation input and output
Proceedings of the 29th conference on Winter simulation
Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation input analysis: joint criterion for factor identification and parameter estimation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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
Reducing parameter uncertainty for stochastic systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
Determining output uncertainty of computer system models
Performance Evaluation
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
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Parameters of statistical distributions that are input to simulations are typically not known with certainty. For existing systems, or variations on existing systems, they are often estimated from field data. Even if the mean of simulation output were estimable exactly as a function of input parameters, there may still be uncertainty about the output mean because inputs are not known precisely. This paper considers the problem of deciding how to allocate resources for additional data collection so that input uncertainty is reduced in a way that effectively reduces uncertainty about the output mean. The optimal solution to the problem in full generality appears to be quite challenging. Here, we simplify the problem with asymptotic approximations in order provide closed-form sampling plans for additional data collection activities. The ideas are illustrated with a simulation of a critical care facility.