Multilevel Monte Carlo Estimation of the Expected Value of Sample Information

DOI10.1137/19M1284981zbMath1455.65007arXiv1909.00549OpenAlexW3098057009MaRDI QIDQ5139350

Howard Thom, Takashi Goda, Michael B. Giles, Tomohiko Hironaka

Publication date: 8 December 2020

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.00549

zbMATH Keywords

decision-making under uncertaintymultilevel Monte Carlonested expectations

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Management decision making, including multiple objectives (90B50)

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Unbiased optimal stopping via the MUSE ⋮ Quantify uncertainty by estimating the probability density function of the output of interest using MLMC based Bayes method ⋮ An efficient estimation of nested expectations without conditional sampling ⋮ Constructing unbiased gradient estimators with finite variance for conditional stochastic optimization ⋮ Unbiased MLMC Stochastic Gradient-Based Optimization of Bayesian Experimental Designs

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