Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure

Rémi Stroh; Julien Bect; Séverine Demeyer; Nicolas Fischer; Emmanuel Vazquez

Conference papers

Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure

Rémi Stroh ^{1, 2, 3} Julien Bect ^{2, 3} Séverine Demeyer ¹ Nicolas Fischer ¹ Emmanuel Vazquez ^{2, 3}

1 LNE - Laboratoire National de Métrologie et d'Essais [Trappes]

2 L2S - Laboratoire des signaux et systèmes

3 GdR MASCOT-NUM - Méthodes d'Analyse Stochastique des Codes et Traitements Numériques

Abstract : A multi-fidelity simulator is a numerical model, in which one of the inputs controls a trade-off between the realism and the computational cost of the simulation. Our goal is to estimate the probability of exceeding a given threshold on a multi-fidelity stochastic simulator. We propose a fully Bayesian approach based on Gaussian processes to compute the posterior probability distribution of this probability. We pay special attention to the hyper-parameters of the model. Our methodology is illustrated on an academic example.

Document type :

Conference papers

Domain :

Statistics [stat] / Methodology [stat.ME]

Statistics [stat] / Computation [stat.CO]

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Submitted on : Wednesday, September 20, 2017 - 10:35:05 AM
Last modification on : Saturday, May 1, 2021 - 3:49:17 AM

Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure

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Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure

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