The ninety-ninth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, May 7, 2026.
2–3 PM — Donatien Rossat ( EDF R&D)
Information Geometry-based Robust Bayesian Analysis
Bayesian inference provides a comprehensive framework for quantifying epistemic uncertainties, and updating them from new information. It relies on updating a so-called prior distribution, which summarizes the level of knowledge about some input parameters. In this work, we introduce a novel sensitivity analysis method to quantify the influence of prior distributions on Bayesian inference outcomes. We define perturbed-law-based sensitivity indices (PLI), which measure the effect of uncertainties in prior specification through controlled perturbations of a reference prior. These perturbations are constructed using the Fisher distance from information geometry, enabling a consistent exploration of a wide range of deviations beyond infinitesimal changes. We further show that these indices can be reformulated as relative variations of rare event probabilities, allowing efficient computation using existing reliability methods. The proposed approach is illustrated on Bayesian inverse problems of varying complexity. Results demonstrate its ability to identify parameters for which prior choices significantly impact Bayesian inference results, while remaining applicable to nonlinear and high-dimensional settings.
References:
- Information Geometry-based Robust Bayesian Analysis, International Journal for Uncertainty Quantification , 2026
Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).
Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)
Practical details: the seminar will be held online using Microsoft Teams.
If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).
You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.
The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.
