The main content of this review article is first to review the main inference tools using Bayes
rule, the maximum entropy principle (MEP), information theory, relative entropy and the …

This paper considers the linear-Gaussian filtering problem in large-dimensions, a framework
in which the Kalman filter (KF) can be computationally prohibitive. As a remedy, a hybrid …

We present, in this paper, a new unsupervised method for joint image super-resolution and
separation between smooth and point sources. For this purpose, we propose a Bayesian …

We consider a nonlinear inverse scattering problem where the goal is to detect breast
cancer from measurements of the scattered field that results from its interaction with a known …

Bayesian approach is nowadays commonly used for inverse problems. Simple prior laws
(Gaussian, Generalized Gaussian, Gauss-Markov and more general Markovian priors) are …

In this paper a nonlinear inverse scattering problem is solved by means of a variational
Bayesian approach. The objective is to detect breast tumor from measurements of the …

Cette thèse préparée au CEA Léti, Minatec Campus, Grenoble, et à l'IMS, Bordeaux, s'
inscrit dans le thème du traitement de l'information pour des données protéomiques. Nous …

In this paper, first the basics of Bayesian inference with a parametric model of the data is
presented. Then, the needed extensions are given when dealing with inverse problems and …

In this work, a new unsupervised Bayesian method for joint image super-resolution and
component separation is introduced. More precisely, we are interested in super-resolution …

Microwave imaging of piece-wise homogeneous objects is considered as a nonlinear
inverse scattering problem. The purpose is to obtain the object contrast (ie a map of its …