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stat.ME2026
A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise
Nicolas Goeman, Pierre-Antoine Thouvenin, Pierre Chainais
Ill-posed inverse problems are encountered in numerous applications, possibly characterized by a highly non-linear forward model, both additive and multiplicative sources of noise,…
stat.ME2026
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems
Maxime Bouton, Pierre-Antoine Thouvenin, Audrey Repetti +1
Markov Chain Monte Carlo (MCMC) algorithms are standard approaches to solve imaging inverse problems and quantify estimation uncertainties, a key requirement in absence of ground-t…