153 citations · 545 across the 29 of their papers we have counts for
10 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…
Proximal nested sampling for high-dimensional Bayesian model selection
Xiaohao Cai, Jason D. McEwen, Marcelo Pereyra
Bayesian model selection provides a powerful framework for objectively comparing models directly from observed data, without reference to ground truth data. However, Bayesian model…
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…
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie
Rémi Laumont, Valentin de Bortoli, Andrés Almansa +3
Since the seminal work of Venkatakrishnan et al. in 2013, Plug & Play (PnP) methods have become ubiquitous in Bayesian imaging. These methods derive Minimum Mean Square Error (MMSE…
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…
Maximum-a-posteriori estimation with Bayesian confidence regions
Marcelo Pereyra
Solutions to inverse problems that are ill-conditioned or ill-posed may have significant intrinsic uncertainty. Unfortunately, analysing and quantifying this uncertainty is very ch…
Bayesian nonlinear hyperspectral unmixing with spatial residual component analysis
Yoann Altmann, Marcelo Pereyra, Stephen McLaughlin
This paper presents a new Bayesian model and algorithm for nonlinear unmixing of hyperspectral images. The model proposed represents the pixel reflectances as linear combinations o…
Proximal Markov chain Monte Carlo algorithms
Marcelo Pereyra
This paper presents a new Metropolis-adjusted Langevin algorithm (MALA) that uses convex analysis to simulate efficiently from high-dimensional densities that are log-concave, a cl…