Learning stochastic model discrepancy

The vast majority of stochastic simulation models are imperfect in that they fail to fully emulate the entirety of real dynamics. Despite this, these imperfect models are still useful in practice, so long as one knows how the model is inexact. This inexactness is measured by a discrepancy between the proposed stochastic model and a true stochastic distribution across multiple values of some decision variables. In this paper, we propose a method to learn the discrepancy of a stochastic simulation using data collected from the system of interest. Our approach is a novel Bayesian framework that addresses the requirements for estimation of probability measures.