no code implementations • 22 May 2024 • François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems.
1 code implementation • 3 Oct 2023 • François Rozet, Gilles Louppe
Data assimilation addresses the problem of identifying plausible state trajectories of dynamical systems given noisy or incomplete observations.
2 code implementations • NeurIPS 2023 • François Rozet, Gilles Louppe
Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem of identifying plausible state trajectories that explain noisy or incomplete observations of stochastic dynamical systems.
1 code implementation • 29 Aug 2022 • Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, Gilles Louppe
In this work, we introduce Balanced Neural Ratio Estimation (BNRE), a variation of the NRE algorithm designed to produce posterior approximations that tend to be more conservative, hence improving their reliability, while sharing the same Bayes optimal solution.
4 code implementations • 13 Oct 2021 • Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Volodimir Begy, Gilles Louppe
We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations.
1 code implementation • 1 Oct 2021 • François Rozet, Gilles Louppe
In many areas of science, complex phenomena are modeled by stochastic parametric simulators, often featuring high-dimensional parameter spaces and intractable likelihoods.