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• W2096689575 abstract "We consider the problem of producing confidence intervals for the mean response of a system represented by a stochastic simulation that is driven by input models that have been estimated from real-world data. Therefore, we want the confidence interval to account for both uncertainty about the input models and stochastic noise in the simulation output; standard practice only accounts for the stochastic noise. To achieve this goal we introduce metamodel-assisted bootstrapping, and illustrate its performance relative to other proposals for dealing with input uncertainty on two queueing examples." @default.
• W2096689575 created "2016-06-24" @default.
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• W2096689575 date "2010-12-05" @default.
• W2096689575 modified "2023-09-23" @default.
• W2096689575 title "A framework for input uncertainty analysis" @default.
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• W2096689575 doi "https://doi.org/10.5555/2433508.2433652" @default.
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