We introduce a novel geometry-informed irreversible perturbation that accelerates
convergence of the Langevin algorithm for Bayesian computation. It is well documented that …

The popularity of Adaptive MCMC has been fueled on the one hand by its success in
applications, and on the other hand, by mathematically appealing and computationally …

We compare numerically the performance of reversible and non-reversible Markov Chain
Monte Carlo algorithms for high-dimensional oil reservoir problems; because of the nature of …

There has been considerable interest in designing Markov chain Monte Carlo algorithms by
exploiting numerical methods for Langevin dynamics, which includes Hamiltonian dynamics …

We study multiscale integrator numerical schemes for a class of stiff stochastic differential
equations (SDEs). We consider multiscale SDEs with potentially multiple attractors that …

Irreversible perturbations in Langevin dynamics have been widely recognized for their role
in accelerating convergence in simulations of multi-modal distributions π (θ). A commonly …

Langevin dynamics are widely used in sampling high-dimensional, non-Gaussian
distributions whose densities are known up to a normalizing constant. In particular, there is …

Cette thèse porte sur l'étude de méthodes numériques pour l'analyse de deux modèles
stochastiques apparaissant dans le contexte de la condensation de Bose-Einstein. Ceux-ci …

This thesis presents new methodologies that lie at the intersection of computational statistics
and computational dynamics. Stochastic differential equations (SDEs) are used to model a …

Novel perturbations for accelerating Langevin samplers Page 1 Novel perturbations for
accelerating Langevin samplers Konstantinos Spiliopoulos Department of Mathematics & …