153 citations · 545 across the 29 of their papers we have counts for
13 papers · 1 filter
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
Teresa Klatzer, Paul Dobson, Yoann Altmann +3
This paper presents a new accelerated proximal Markov chain Monte Carlo methodology to perform Bayesian inference in imaging inverse problems with an underlying convex geometry. Th…
Efficient Bayesian computation for low-photon imaging problems
Savvas Melidonis, Paul Dobson, Yoann Altmann +2
This paper studies a new and highly efficient Markov chain Monte Carlo (MCMC) methodology to perform Bayesian inference in low-photon imaging problems, with particular attention to…
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution
Marcelo Pereyra, Luis A. Vargas-Mieles, Konstantinos C. Zygalakis
Developing efficient Bayesian computation algorithms for imaging inverse problems is challenging due to the dimensionality involved and because Bayesian imaging models are often no…
Bayesian model selection for unsupervised image deconvolution with structured Gaussian priors
Benjamin Harroué, Jean-François Giovannelli, Marcelo Pereyra
This paper considers the objective comparison of stochastic models to solve inverse problems, more specifically image restoration. Most often, model comparison is addressed in a su…
Accelerating proximal Markov chain Monte Carlo by using an explicit stabilised method
Luis Vargas, Marcelo Pereyra, Konstantinos C. Zygalakis
We present a highly efficient proximal Markov chain Monte Carlo methodology to perform Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo approach…
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Valentin De Bortoli, Alain Durmus, Marcelo Pereyra +1
Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as marginal likelihoods arising in p…
Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau
Alain Durmus, Eric Moulines, Marcelo Pereyra
Modern imaging methods rely strongly on Bayesian inference techniques to solve challenging imaging problems. Currently, the predominant Bayesian computation approach is convex opti…
Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation
Marcelo Pereyra, Steve McLaughlin
This paper presents a new Bayesian estimation technique for hidden Potts-Markov random fields with unknown regularisation parameters, with application to fast unsupervised K-class…
Bayesian computation: a perspective on the current state, and sampling backwards and forwards
Peter J. Green, Krzysztof Łatuszyński, Marcelo Pereyra +1
The past decades have seen enormous improvements in computational inference based on statistical models, with continual enhancement in a wide range of computational tools, in compe…
Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing
Yoann Altmann, Marcelo Pereyra, Jose Bioucas-Dias
This paper presents a new Bayesian collaborative sparse regression method for linear unmixing of hyperspectral images. Our contribution is twofold; first, we propose a new Bayesian…
Discussion of "Geodesic Monte Carlo on Embedded Manifolds"
Simon Byrne, Mark Girolami, Persi Diaconis +9
Contributed discussion and rejoinder to "Geodesic Monte Carlo on Embedded Manifolds" (arXiv:1301.6064)
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm
Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia +1
This paper addresses the problem of estimating the Potts parameter B jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Sta…
Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models
Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia +1
This report considers the problem of computing the Cramer-Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of i…