ONLINE QUANTIFICATION OF INPUT UNCERTAINTY FOR PARAMETRIC MODELS

It has become increasingly important to assimilate "online data" that arrive sequentially in time for real-time decision. Input uncertainty quantification in stochastic simulation has been developed extensively for batch data that are available all at once, but little has been studied for online data. In this paper, we propose a computationally efficient method to incorporate online data in real time for input uncertainty quantification of parametric models. We show finite-sample bounds and asymptotic convergence for the proposed method, and demonstrate its performance on a simple numerical example.