Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure
Methodology
2021-03-31 v1 Computation
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.
Keywords
Cite
@article{arxiv.1709.06896,
title = {Integrating hyper-parameter uncertainties in a multi-fidelity Bayesian model for the estimation of a probability of failure},
author = {Rémi Stroh and Julien Bect and Séverine Demeyer and Nicolas Fischer and Emmanuel Vazquez},
journal= {arXiv preprint arXiv:1709.06896},
year = {2021}
}