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arXiv · 1709.06896

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
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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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Rémi Stroh, Julien Bect, Séverine Demeyer, Nicolas Fischer, Emmanuel Vazquez. 2017-09-20. Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure. https://doi.org/10.1142/9789813274303_0035

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