19 citations · 51 across the 6 of their papers we have counts for
5 papers · 1 filter
Proximal nested sampling with data-driven priors for physical scientists
Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price +2
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for mode…
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms
Matthew Holden, Marcelo Pereyra, Konstantinos C. Zygalakis
This paper proposes a new methodology for performing Bayesian inference in imaging inverse problems where the prior knowledge is available in the form of training data. Following t…
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments
Ana F. Vidal, Valentin De Bortoli, Marcelo Pereyra +1
Many imaging problems require solving an inverse problem that is ill-conditioned or ill-posed. Imaging methods typically address this difficulty by regularising the estimation prob…
Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization
Audrey Repetti, Marcelo Pereyra, Yves Wiaux
We propose a Bayesian uncertainty quantification method for large-scale imaging inverse problems. Our method applies to all Bayesian models that are log-concave, where maximum-a-po…
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
Nicolas Brosse, Alain Durmus, Éric Moulines +1
This paper presents a detailed theoretical analysis of the Langevin Monte Carlo sampling algorithm recently introduced in Durmus et al. (Efficient Bayesian computation by proximal…