Bayesian sequential calibration using detailed sample paths

A simplified simulation model is often used to guide the decision-making for a real complex stochastic system. To faithfully assess the mean performance of the real system, it is necessary to calibrate the simulation model. Existing calibration approaches are typically built on the summary statistics of simulation outputs and ignore the dynamic information carried by the detailed sample paths. In this paper, we develop a new calibration approach incorporating the detailed output sample paths in a sequential manner. Our theoretical development and empirical study demonstrate that we can efficiently use the simulation resources and achieve better calibration accuracy by exploring the system dynamic behaviors.