Abstract We study the Unadjusted Langevin Algorithm (ULA) for sampling from a probability
distribution $\nu= e^{-f} $ on $\R^ n $. We prove a convergence guarantee in Kullback …

Many parameter estimation problems arising in applications can be cast in the framework of
Bayesian inversion. This allows not only for an estimate of the parameters, but also for the …

Ensemble Kalman methods are widely used for state estimation in the geophysical sciences.
Their success stems from the fact that they take an underlying (possibly noisy) dynamical …

Fokker–Planck equations are extensively employed in various scientific fields as they
characterise the behaviour of stochastic systems at the level of probability density functions …

Bayesian inference problems require sampling or approximating high-dimensional
probability distributions. The focus of this paper is on the recently introduced Stein …

We consider Bayesian inference for large-scale inverse problems, where computational
challenges arise from the need for repeated evaluations of an expensive forward model …

We introduce a new mean-field ODE and corresponding interacting particle systems (IPS)
for sampling from an unnormalized target density. The IPS are gradient-free, available in …

For algorithms based on interacting particle systems that admit a mean-field description,
convergence analysis is often more accessible at the mean-field level. In order to transpose …

This paper is focused on the optimization approach to the solution of inverse problems. We
introduce a stochastic dynamical system in which the parameter-to-data map is embedded …

We propose a novel method for sampling and optimization tasks based on a stochastic
interacting particle system. We explain how this method can be used for the following two …