Titre :

A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems

Auteur(s) :

Thouvenin, Pierre Antoine [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Repetti, Audrey [Auteur]
Heriot-Watt University [Edinburgh] [HWU]
School of Mathematical and Computer Sciences [MATHEMATICS DEPARTMENT OF HERIOT-WATT UNIVERSITY]
Chainais, Pierre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la revue :

Journal of Computational and Graphical Statistics

Pagination :

814–832

Éditeur :

Taylor & Francis

Date de publication :

2023-11-15

ISSN :

1061-8600

Mot(s)-clé(s) en anglais :

MCMC algorithm
Bayesian inference
block-coordinate algorithm
distributed architecture
high dimensional imaging inverse problems

Discipline(s) HAL :

Statistiques [stat]/Calcul [stat.CO]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

Sampling-based algorithms are classical approaches to perform Bayesian inference in inverse problems. They provide estimators with the associated credibility intervals to quantify the uncertainty on the estimators. Although ...
Lire la suite >Sampling-based algorithms are classical approaches to perform Bayesian inference in inverse problems. They provide estimators with the associated credibility intervals to quantify the uncertainty on the estimators. Although these methods hardly scale to high dimensional problems, they have recently been paired with optimization techniques, such as proximal and splitting approaches, to address this issue. Such approaches pave the way to distributed samplers, splitting computations to make inference more scalable and faster. We introduce a distributed Split Gibbs sampler (SGS) to efficiently solve such problems involving distributions with multiple smooth and non-smooth functions composed with linear operators. The proposed approach leverages a recent approximate augmentation technique reminiscent of primal-dual optimization methods. It is further combined with a block-coordinate approach to split the primal and dual variables into blocks, leading to a distributed block-coordinate SGS. The resulting algorithm exploits the hypergraph structure of the involved linear operators to efficiently distribute the variables over multiple workers under controlled communication costs. It accommodates several distributed architectures, such as the Single Program Multiple Data and client-server architectures. Experiments on a large image deblurring problem show the performance of the proposed approach to produce high quality estimates with credibility intervals in a small amount of time. Supplementary material to reproduce the experiments is available online.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Inférence rapide et contrôle de l'incertitude: applications aux observations astrophysiques.
ULNE

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL