Steps to implement Bayesian input distribution selection
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Input uncertainty: accounting for parameter uncertainty in simulation input modeling
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
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
Proceedings of the 35th conference on Winter simulation: driving innovation
Reducing parameter uncertainty for stochastic systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
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
Input uncertainty in outout analysis
Proceedings of the Winter Simulation Conference
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
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Taking into account input-model, input-parameter, and stochastic uncertainties inherent in many simulations, our Bayesian approach to input modeling yields valid point and confidence-interval estimators for a selected posterior mean response. Exploiting prior information to specify the prior plausibility of each candidate input model and to construct prior distributions on the model's parameters, we combine this information with the likelihood function of sample data to compute posterior model probabilities and parameter distributions. Our Bayesian Simulation Replication Algorithm involves: (a) estimating parameter uncertainty by sampling from the posterior parameter distributions on selected runs; (b) estimating stochastic uncertainty by multiple independent replications of those runs; and (c) estimating model uncertainty by weighting the results of (a) and (b) using the corresponding posterior model probabilities. We allocate runs in (a) and (b) to minimize final estimator variance subject to a computing-budget constraint. An experimental performance evaluation demonstrates the advantages of this approach.