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- W2913404612 abstract "It has become increasingly important to assimilate 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." @default.
- W2913404612 created "2019-02-21" @default.
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- W2913404612 date "2018-12-09" @default.
- W2913404612 modified "2023-09-24" @default.
- W2913404612 title "Online quantification of input uncertainty for parametric models" @default.
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- W2913404612 doi "https://doi.org/10.5555/3320516.3320711" @default.
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