Empirical model-building and response surface
Empirical model-building and response surface
Response surface methodology: 1966–1988
Technometrics
Bayesian analysis for simulation input and output
Proceedings of the 29th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Accounting for input model and parameter uncertainty in simulation
Proceedings of the 33nd conference on Winter simulation
Reducing input parameter uncertainty for simulations
Proceedings of the 33nd conference on Winter simulation
Calculation of confidence intervals for simulation output
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
Reliable simulation with input uncertainties using an interval-based approach
Proceedings of the 40th Conference on Winter Simulation
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
Proceedings of the Winter Simulation Conference
Capturing parameter uncertainty in simulations with correlated inputs
Proceedings of the Winter Simulation Conference
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The design of many production and service systems is informed by stochastic model analysis. But the parameters of statistical distributions of stochastic models are rarely known with certainty, and are often estimated from field data. Even if the mean system performance is a known function of the model's parameters, there may still be uncertainty about the mean performance because the parameters are not known precisely. Several methods have been proposed to quantify this uncertainty, but data sampling plans have not yet been provided to reduce parameter uncertainty in a way that effectively reduces uncertainty about mean performance. The optimal solution is challenging, so we use asymptotic approximations to obtain closed-form results for sampling plans. The results apply to a wide class of stochastic models, including situations where the mean performance is unknown but estimated with simulation. Analytical and empirical results for the M/M/1 queue, a quadratic response-surface model, and a simulated critical care facility illustrate the ideas.