WSC '94 Proceedings of the 26th conference on Winter simulation
Bootstrap methods in computer simulation experiments
WSC '95 Proceedings of the 27th 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
Bayesian model selection when the number of components is unknown
Proceedings of the 30th conference on Winter simulation
Problems in Bayesian analysis of stochastic simulation
WSC '86 Proceedings of the 18th 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
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2
Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
Accounting for input model and parameter uncertainty in simulation
Proceedings of the 33nd conference on Winter simulation
Resampling methods for input modeling
Proceedings of the 33nd conference on Winter simulation
M/G/c/K blocking probability models and system performance
Performance Evaluation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Heavy-Traffic Limits for Loss Proportions in Single-Server Queues
Queueing Systems: Theory and Applications
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 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
Verification and validation of simulation models
Proceedings of the Winter Simulation Conference
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
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Simulation output clearly depends on the form of the input distributions used to drive the model. Often these input distributions are fitted using finite samples of real-world data. The finiteness of the samples introduces errors in the input distributions, affecting the output. Yet this propagation of input model uncertainty to output uncertainty is rarely considered in simulation output analysis. This tutorial presents a discussion of input uncertainty issues and recently developed methodological approaches, set in the context of input uncertainty methods proposed over the past twenty years.