Accounting for parameter uncertainty in simulation input modeling

We formulate and evaluate a Bayesian approach to probabilistic input modeling. Taking into account the parameter and stochastic uncertainties inherent in most simulations, this approach yields valid predictive inferences about the output quantities of interest. We use prior information to construct prior distributions on the input-model parameters. Combining this prior information with the likelihood function of sample data observed on the input processes, we compute the posterior parameter distributions using Bayes' rule. This leads to a Bayesian simulation replication algorithm in which: (a) we estimate the parameter uncertainty by sampling from the posterior distribution of the input model's parameters on selected simulation runs; and (b) we estimate the stochastic uncertainty by multiple independent replications of those selected runs. We also formulate some performance evaluation criteria that are reasonable within both the Bayesian and frequentist paradigms. An experimental performance evaluation demonstrates the advantages of the Bayesian approach versus conventional frequentist techniques.