M
Marcelo Pereyra
Researcher at Heriot-Watt University
Publications - 89
Citations - 1951
Marcelo Pereyra is an academic researcher from Heriot-Watt University. The author has contributed to research in topics: Bayesian inference & Markov chain Monte Carlo. The author has an hindex of 21, co-authored 81 publications receiving 1493 citations. Previous affiliations of Marcelo Pereyra include University of Bristol & University of Toulouse.
Papers
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Bayesian computation: a summary of the current state, and samples backwards and forwards
TL;DR: The difficulties of modelling and then handling ever more complex datasets most likely call for a new type of tool for computational inference that dramatically reduces the dimension and size of the raw data while capturing its essential aspects.
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Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau
TL;DR: In this paper, a Markov chain Monte Carlo (MCMC) method is proposed for high-dimensional models that are log-concave and nonsmooth, a class of models that is central in imaging sciences.
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A Survey of Stochastic Simulation and Optimization Methods in Signal Processing
Marcelo Pereyra,Philip Schniter,Emilie Chouzenoux,Jean-Christophe Pesquet,Jean-Yves Tourneret,Alfred O. Hero,Steve McLaughlin +6 more
TL;DR: The paper addresses a variety of high-dimensional Markov chain Monte Carlo methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms.
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Proximal Markov chain Monte Carlo algorithms
TL;DR: In this paper, a new Metropolis-adjusted Langevin algorithm (MALA) is proposed to simulate efficiently from high-dimensional densities that are log-concave, a class of probability distributions that is widely used in modern highdimensional statistics and data analysis.
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Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm
TL;DR: This paper addresses the problem of estimating the Potts parameter β jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm with results that are as good as those obtained with the actual value of β.