153 citations · 545 across the 29 of their papers we have counts for
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stat.CO2016★ 8 cited
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…
math.ST2016
Revisiting maximum-a-posteriori estimation in log-concave models
Marcelo Pereyra
Maximum-a-posteriori (MAP) estimation is the main Bayesian estimation methodology in imaging sciences, where high dimensionality is often addressed by using Bayesian models that ar…
stat.ME2016
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…