Bias and Variance in the Bayesian Subset Simulation Algorithm

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.
Type de document :
Communication dans un congrès
2016 SIAM Conference on Uncertainty Quantification, Apr 2016, Lausanne, Switzerland. 〈http://meetings.siam.org/sess/dsp_talk.cfm?p=74199〉
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01377732
Contributeur : Julien Bect <>
Soumis le : vendredi 7 octobre 2016 - 14:59:35
Dernière modification le : vendredi 20 octobre 2017 - 01:17:47

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  • HAL Id : hal-01377732, version 1

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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. 〈http://meetings.siam.org/sess/dsp_talk.cfm?p=74199〉. 〈hal-01377732〉

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