WSC '94 Proceedings of the 26th conference on Winter simulation
Computational structure of a performance assessment involving stochastic and subjective uncertainty
WSC '96 Proceedings of the 28th conference on Winter simulation
Five-stage procedure for the evaluation of simulation models through statistical techniques
WSC '96 Proceedings of the 28th 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
Mathematics of Operations Research
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
Mathematics and hybrid modeling: mathematics for simulation
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Analysis of simulation experiments by bootstrap resampling
Proceedings of the 33nd conference on Winter simulation
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
Monte carlo computation of conditional expectation quantiles
Monte carlo computation of conditional expectation quantiles
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
Simulation input modeling: a kernel approach to estimating the density of a conditional expectation
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
A Bayesian approach to analysis of limit standards
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Reliable simulation with input uncertainties using an interval-based approach
Proceedings of the 40th Conference on Winter Simulation
A simple model for assessing output uncertainty in stochastic simulation systems
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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
Towards simulation-based robust computational scientific discovery systems
Proceedings of the 2011 Summer Computer Simulation Conference
Robust Simulation of Global Warming Policies Using the DICE Model
Management Science
Multiple input and multiple output simulation metamodeling using Bayesian networks
Proceedings of the Winter Simulation Conference
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
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An input model is a collection of distributions together with any associated parameters that are used as primitive inputs in a simulation model. Input model uncertainty arises when one is not completely certain what distributions and/or parameters to use. This tutorial attempts to provide a sense of why one should consider input uncertainty and what methods can be used to deal with it.