An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension

An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension

In this paper, we are interested in Bayesian inverse problems where either the data fidelity term or the prior distribution is Gaussian or driven from a hierarchical Gaussian model. Generally, Markov chain Monte Carlo (MCMC) algorithms allow us to generate sets of samples that are employed to infer...

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Bibliographic Details
Main Authors: Yosra Marnissi, Emilie Chouzenoux, Amel Benazza-Benyahia, Jean-Christophe Pesquet
Format: Article
Language:English
Published: MDPI AG 2018-02-01
Series:Entropy
Subjects:
data augmentation
auxiliary variables
MCMC
Gaussian models
large scale problems
Bayesian methods
Online Access:http://www.mdpi.com/1099-4300/20/2/110

Internet

http://www.mdpi.com/1099-4300/20/2/110

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