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On variable splitting for Markov chain Monte Carlo

Vono, Maxime and Dobigeon, Nicolas and Chainais, Pierre On variable splitting for Markov chain Monte Carlo. (2019) In: Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS 2019), 1 April 2019 - 4 April 2019 (Toulouse, France).

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Abstract

Variable splitting is an old but widely used technique whichaims at dividing an initial complicated optimization problem into simplersub-problems. In this work, we take inspiration from this variable splitting idea in order to build efficient Markov chain Monte Carlo(MCMC) algorithms. Starting from an initial complex target distribution,auxiliary variables are introduced such that the marginal distributionof interest matches the initial one asymptotically. In addition to havetheoretical guarantees, the benefits of such an asymptotically exact dataaugmentation (AXDA) are fourfold: (i) easier-to-sample full conditionaldistributions, (ii) possibility to embed while accelerating state-of-the-artMCMC approaches, (iii) possibility to distribute the inference and (iv)to respect data privacy issues. The proposed approach is illustrated onclassical image processing and statistical learning problems.

Item Type:Conference or Workshop Item (Paper)
Audience (conference):National conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Ecole Centrale de Lille (FRANCE)
Other partners > Université de Lille (FRANCE)
Laboratory name:
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Deposited By: IRIT IRIT
Deposited On:10 Dec 2019 15:29

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