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

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.
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Communication dans un congrès
11th International Conference on Advanced Mathematical and Computational Tools in Metrology and Testing (AMTCM 2017) , Aug 2017, Glasgow, United Kingdom. 2017
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Soumis le : mercredi 20 septembre 2017 - 10:35:05
Dernière modification le : jeudi 5 avril 2018 - 12:30:05

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  • HAL Id : hal-01590748, version 1
  • ARXIV : 1709.06896

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Rémi Stroh, Julien Bect, Séverine Demeyer, Nicolas Fischer, Emmanuel Vazquez. Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure. 11th International Conference on Advanced Mathematical and Computational Tools in Metrology and Testing (AMTCM 2017) , Aug 2017, Glasgow, United Kingdom. 2017. 〈hal-01590748〉

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