This paper tackles the problem of image deconvolution with joint estimation of point spread
function (PSF) parameters and hyperparameters. Within a Bayesian framework, the solution …

Data augmentation, by the introduction of auxiliary variables, has become an ubiquitous
technique to improve convergence properties, simplify the implementation or reduce the …

This paper is devoted to the problem of sampling Gaussian distributions in high dimension.
Solutions exist for two specific structures of inverse covariance: sparse and circulant. The …

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 …

In this paper we provide an algorithm adapted to variational Bayesian approximation. The
main contribution is to transpose a classical iterative algorithm of optimization in the metric …

We describe regularized methods for image reconstruction and focus on the question of
hyperparameter and instrument parameter estimation, ie unsupervised and myopic …

This paper deals with Gibbs samplers that include high dimensional conditional Gaussian
distributions. It proposes an efficient algorithm that avoids the high dimensional Gaussian …

Markov Chain Monte Carlo sampling algorithms are efficient Bayesian tools to explore
complicated posterior distributions. However, sampling in large scale problems remains a …

This paper constructs bounds on the expected value of a scalar function of a random vector.
The bounds are obtained using an optimization method, which can be computed efficiently …

In this paper we provide a new algorithm allowing to solve a variational Bayesian issue
which can be seen as a functional optimization problem. The main contribution of this paper …