Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
Reducing input parameter uncertainty for simulations
Proceedings of the 33nd conference on Winter simulation
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
Proceedings of the 35th conference on Winter simulation: driving innovation
Very large fractional factorial and central composite designs
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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
WSC '04 Proceedings of the 36th conference on Winter simulation
Introduction to modeling and generating probabilistic input processes for simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 38th conference on Winter simulation
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A Bayesian approach to analysis of limit standards
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 40th Conference on Winter Simulation
Comparison of Bayesian priors for highly reliable limit models
Proceedings of the 40th Conference on Winter Simulation
Reliable simulation with input uncertainties using an interval-based approach
Proceedings of the 40th Conference on Winter Simulation
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
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
Introduction to modeling and generating probabilistic input processes for simulation
Winter Simulation Conference
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
Robust Simulation of Global Warming Policies Using the DICE Model
Management Science
Input uncertainty in outout analysis
Proceedings of the Winter Simulation Conference
A quick assessment of input uncertainty
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Capturing parameter uncertainty in simulations with correlated inputs
Proceedings of the Winter Simulation Conference
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
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A number of authors have identified problematic issues with techniques used in current simulation practice for selecting probability distributions and their parameters for input to stochastic simulations. A major goal of this paper is to address some of those issues by presenting a self-consistent evaluation of the uncertainty about the mean value of the simulation output, when there is uncertainty in both the parameters and functional form of input distributions (structural uncertainty), and uncertainty due to the stochastic nature of simulation output (stochastic uncertainty), as is common in simulation practice. The analysis leads to an algorithm for randomly sampling input distributions and parameters before each simulation replication, using a Bayesian posterior distribution for input distributions and parameters, given historical data. Mechanisms for addressing issues of importance to the discrete-event simulation community are illustrated by example, such as the specification of prior distributions, and analysis for shifted distributions.