Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
WSC '94 Proceedings of the 26th conference on Winter simulation
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
Steps to implement Bayesian input distribution selection
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd 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
Input uncertainty: accounting for parameter uncertainty in simulation input modeling
Proceedings of the 33nd conference on Winter simulation
Reducing input parameter uncertainty for simulations
Proceedings of the 33nd conference on Winter simulation
Resampling methods for input modeling
Proceedings of the 33nd conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Calculation of confidence intervals for simulation output
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation input analysis: collecting data and estimating parameters for input distributions
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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
Simulation input modeling: a kernel approach to estimating the density of a conditional expectation
Proceedings of the 35th conference on Winter simulation: driving innovation
Stochastic Kriging for Simulation Metamodeling
Operations Research
On the Accuracy of Ad Hoc Distributed Simulations for Open Queueing Network
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
Input uncertainty in outout analysis
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Metamodel variability analysis combining bootstrapping and validation techniques
Proceedings of the Winter Simulation Conference
Multiple input and multiple output simulation metamodeling using Bayesian networks
Proceedings of the Winter Simulation Conference
Machine learning with operational costs
The Journal of Machine Learning Research
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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.