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ONLINE QUANTIFICATION OF INPUT UNCERTAINTY FOR PARAMETRIC MODELS

12 citations0/0 files storedWinter Simulation Conference

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Probabilistic and Robust Engineering Design Simulation Techniques and Applications Gaussian Processes and Bayesian Inference

Abstract

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.

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2004Feynman-Kac Formulae 2009Stochastic Kriging for Simulation Metamodeling 2001Input Distribution Selection for Simulation Experiments: Accounting for Input Uncertainty 1997Sensitivity of computer simulation experiments to errors in input data 2016Robust Sensitivity Analysis for Stochastic Systems 2003Accounting for Parameter Uncertainty in Simulation Input Modeling 2013Quantifying Input Uncertainty via Simulation Confidence Intervals 2014A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation

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