Proximal Langevin sampling with inexact proximal map**
In order to solve tasks like uncertainty quantification or hypothesis tests in Bayesian imaging
inverse problems, we often have to draw samples from the arising posterior distribution. For …
inverse problems, we often have to draw samples from the arising posterior distribution. For …
Distribution-free uncertainty quantification for inverse problems: application to weak lensing mass map**
Aims. In inverse problems, the aim of distribution-free uncertainty quantification (UQ) is to
obtain error bars in the reconstruction with coverage guarantees that are independent of any …
obtain error bars in the reconstruction with coverage guarantees that are independent of any …
Proximal Interacting Particle Langevin Algorithms
We introduce a class of algorithms, termed Proximal Interacting Particle Langevin Algorithms
(PIPLA), for inference and learning in latent variable models whose joint probability density …
(PIPLA), for inference and learning in latent variable models whose joint probability density …
Coupling Analysis of the Asymptotic Behaviour of a Primal-Dual Langevin Algorithm
In this paper, we analyze a recently proposed algorithm for the problem of sampling from
probability distributions $\mu^\ast $ in $\mathbb {R}^ d $ with a Lebesgue density and …
probability distributions $\mu^\ast $ in $\mathbb {R}^ d $ with a Lebesgue density and …