Efficient sampling from a high-dimensional Gaussian distribution is an old but high-stakes
issue. Vanilla Cholesky samplers imply a computational cost and memory requirements that …

We propose a Bayesian uncertainty quantification method for large-scale imaging inverse
problems. Our method applies to all Bayesian models that are log-concave, where maximum …

Abstract Double Electron-Electron Resonance (DEER) spectroscopy is a solid-state pulse
Electron Paramagnetic Resonance (EPR) experiment that measures distances between …

This paper presents two hierarchical Bayesian methods for performing maximum-a-
posteriori inference when the value of the regularisation parameter is unknown. The …

We investigate the use of learning approaches to handle Bayesian inverse problems in a
computationally efficient way when the signals to be inverted present a moderately high …

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

In this paper, we address the numerical posterior error control problem for the Bayesian
approach to inverse problems or recently known as Bayesian Uncertainty Quantification …

Using standard techniques in Probability theory we prove a series of results relevant in the
theory of Bayesian uncertainty quantification (UQ). Using the approach, found in the …

The paper addresses Bayesian inferences in inverse problems with uncertainty
quantification involving a computationally expensive forward map associated with solving a …

An original 3-D radar imaging system is presented for radar cross section (RCS) analysis, ie,
to identify and characterize the radar backscattering components of an object. Based on a 3 …