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Improving prediction from stochastic simulation via model ...
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由 H Lam 著作2017被引用 8 次 — We investigate a framework to statistically learn these discrepancies under the presence of data on past implemented system configurations, which allows us to ...
Improving prediction from stochastic simulation via model ...
Columbia University
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Columbia University
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由 WKV Chan 著作被引用 8 次 — We investigate a framework to statistically learn these discrepancies under the presence of data on past implemented system configurations, which allows us to ...
[PDF] Improving prediction from stochastic simulation via model ...
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This work investigates a framework to statistically learn discrepancies under the presence of data on past implemented system configurations, which allows ...
Improving prediction from stochastic simulation via model ...
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Often times, however, the model, even when richly parametrized, could still deviate from the true dynamics and incur a systematic model error -a discrepancy ...
Improving prediction from stochastic simulation via model ...
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TL;DR: This work investigates a framework to statistically learn discrepancies under the presence of data on past implemented system configurations, which ...
Improving prediction from stochastic simulation via model ...
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Henry Lam, Xinyu Zhang, Matthew Plumlee : Improving prediction from stochastic simulation via model discrepancy learning. WSC 2017: 1808-1819.
[PDF] Learning stochastic model discrepancy
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Improving prediction from stochastic simulation via model discrepancy learning · Computer Science, Engineering. Online World Conference on Soft Computing in…
Henry Lam
Columbia University
http://www.columbia.edu › ~khl2114
Columbia University
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Van Oyen, Winter Simulation Conference (WSC) 2017. · Improving prediction from stochastic simulation via model discrepancy learning, with M. Plumlee and X ...
LEARNING STOCHASTIC MODEL DISCREPANCY
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由 M Plumlee 著作2016被引用 7 次 — 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 ...
Improving stochastic models by smart denoising and latent ...
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由 J Jelenčič 著作2024 — This paper introduces an innovative deep learning-based optimization method specifically designed for data derived from stochastic processes ...