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Conference Papers Year : 2014

Majorize-Minimize adapted Metropolis-Hastings algorithm. Application to multichannel image recovery

Abstract

One challenging task in MCMC methods is the choice of the proposal density. It should ideally provide an accurate approximation of the target density with a low computational cost. In this paper, we are interested in Langevin diffusion where the proposal accounts for a directional component. We propose a novel method for tuning the related drift term. This term is preconditioned by an adaptive matrix based on a Majorize-Minimize strategy. This new procedure is shown to exhibit a good performance in a multispectral image restoration example.
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Dates and versions

hal-01077273 , version 1 (24-10-2014)

Identifiers

  • HAL Id : hal-01077273 , version 1

Cite

Yosra Marnissi, Amel Benazza-Benyahia, Emilie Chouzenoux, Jean-Christophe Pesquet. Majorize-Minimize adapted Metropolis-Hastings algorithm. Application to multichannel image recovery. 22th European Signal Processing Conference (EUSIPCO 2014), Sep 2014, Lisbon, Portugal. ⟨hal-01077273⟩
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