Markov chain Monte Carlo (MCMC) methods are an important class of computation techniques to solve Bayesian inference problems. Much research has been dedicated to scale these algorithms in high-dimensional settings by relying on powerful optimization tools such as gradient information or proximity operators. In a similar vein, this paper proposes a new Bayesian hierarchical model to solve large scale inference problems by taking inspiration from variable splitting methods. Similarly to the latter, the derived Gibbs sampler enables to divide the initial sampling task into simpler ones. In addition, the proposed Bayesian framework can lead to a faster sampling scheme than state-of-the-art methods by embedding them. The interest of the proposed methodology is illustrated on often-studied image processing problems.
By Sylvain Lefèbvre
By Julien Rabin
By Ron Kimmel
By Michael Lindenbaum
By Cécile Louchet
By Michael Unser
By Hermine Biermé
By Pierre Chainais
By Jérémie Bigot
By Gersende Fort
By Remco Duits
By Stéphanie Allassonnière
By Charles Kervrann
By Xavier Descombes
By Marie-Paule Cani
By Irène Kaltenmark
By Joan Glaunès
By Marcelo Pereyra
By Alain Trouvé
By Giulio Biroli
By Giulio Biroli
By Gérard Ben Arous
By Gérard Ben Arous
