Adaptive importance sampling (AIS) methods are increasingly used for the approximation of
distributions and related intractable integrals in the context of Bayesian inference …

Importance sampling (IS) is a powerful Monte Carlo methodology for the approximation of
intractable integrals, very often involving a target probability distribution. The performance of …

In this paper, we examine the computational complexity of sampling from a Bayesian
posterior (or pseudo-posterior) using the Metropolis-adjusted Langevin algorithm (MALA) …

The dimension and the complexity of inference problems have dramatically increased in
statistical signal processing. It thus becomes mandatory to design improved proposal …

We present a systematic construction of FEM-based dimension-independent (discretization-
invariant) Markov chain Monte Carlo (MCMC) approaches to explore PDE-constrained …

Massive black hole binaries are the primary source of gravitational waves (GWs) for the
future eLISA observatory. The detection and parameter estimation of these sources to high …

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 …

This paper considers the problem of estimating point-like pollution sources of groundwater
layers. To cope with the ill-posed character of this problem, a parametric Bayesian …

Adaptive optics (AO) corrected image restoration is particularly difficult, as it suffers from the
lack of knowledge on the point spread function (PSF) in addition to usual difficulties. An …

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