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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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https://hal.archives-ouvertes.fr/hal-01077273
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Submitted on : Friday, October 24, 2014 - 12:11:10 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:06 PM
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  • HAL Id : hal-01077273, version 1

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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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