3 papers
stat.ML2026
Mixed Poisson diffusion models and application to posterior sampling
Thibaut Pellerin, Alain Durmus, Elinor Berger +1
Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely on Gaussian noising processes,…
stat.CO2026
Jensen-Bregman split Gibbs sampling
Nicolas Dobigeon, Mame Diarra Fall, Eric Barat
Split Gibbs sampling (SGS) is a versatile tool to sample from complex posterior distributions, such as those arising in imaging inverse problems. However, existing SGS instances ar…
stat.CO2025
Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problems
Elhadji Cisse Faye, Mame Diarra Fall, Nicolas Dobigeon +1
This paper proposes a novel Bayesian framework for solving Poisson inverse problems by devising a Monte Carlo sampling algorithm which accounts for the underlying non-Euclidean geo…