The vast majority of stochastic simulation models are imperfect in that they fail to exactly emulate real system dynamics. The inexactness of the simulation model, or model discrepancy, can impact the predictive accuracy and usefulness of the simulation for decision-making. This paper proposes a systematic framework to integrate data from both the simulation responses and the real system responses to learn this discrepancy and quantify the resulting uncertainty. Our framework addresses the theoretical and computational requirements for stochastic estimation in a Bayesian setting. It involves an optimization-based procedure to compute confidence bounds on the target outputs that elicit desirable large-sample statistical properties. We illustrate the practical value of our framework with a call center example and a manufacturing line case study.
|Original language||English (US)|
|State||Published - Jul 20 2017|
- Bayesian methods
- Model calibration
ASJC Scopus subject areas