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Bias and Variance in the Bayesian Subset Simulation Algorithm

Julien Bect 1, 2 Roman Sueur 3 Emmanuel Vazquez 1, 2
1 L2S - Laboratoire des signaux et systèmes
2 GdR MASCOT-NUM - Méthodes d'Analyse Stochastique des Codes et Traitements Numériques
3 EDF R&D MRI - Management des Risques Industriels
Abstract : The Bayesian Subset Simulation (BSS) algorithm is a recently proposed approach, based on Sequential Monte Carlo simulation and Gaussian process modeling, for the estimation of the probability that $f(X)$ exceeds some thresold $u$ when $f$ is expensive to evaluate and $P(f(X)>u)$ is small. We discuss in this talk the bias an variance of the BSS algorithm, and propose a variant where the bias-variance trade-off is automatically tuned.
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Conference papers
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01377732
Contributor : Julien Bect <>
Submitted on : Friday, October 7, 2016 - 2:59:35 PM
Last modification on : Saturday, May 1, 2021 - 3:48:36 AM

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  • HAL Id : hal-01377732, version 1

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CNRS | SUP_LSS | CENTRALESUPELEC | SUP_SIGNAUX | EDF | GS-COMPUTER-SCIENCE | GS-ENGINEERING

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Julien Bect, Roman Sueur, Emmanuel Vazquez. Bias and Variance in the Bayesian Subset Simulation Algorithm. 2016 SIAM Conference on Uncertainty Quantification, Apr 2016, Lausanne, Switzerland. ⟨hal-01377732⟩

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