Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

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

Abstract Stein Variational Gradient Descent (SVGD) is an important alternative to the
Langevin-type algorithms for sampling from probability distributions of the form $\pi …

Abstract Stein Variational Gradient Descent (SVGD) is a popular particle-based variational
inference algorithm with impressive empirical performance across various domains …

Abstract Stein Variational Gradient Descent (SVGD) is an algorithm for sampling from a
target density which is known up to a multiplicative constant. Although SVGD is a popular …

The aim of this paper is twofold. Based on the geometric Wasserstein tangent space, we first
introduce Wasserstein steepest descent flows. These are locally absolutely continuous …

We study a variant of the dynamical optimal transport problem in which the energy to be
minimised is modulated by the covariance matrix of the distribution. Such transport metrics …

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

Motivated by the challenge of incorporating data into misspecified and multiscale dynamical
models, we study a McKean–Vlasov equation that contains the data stream as a common …

Particle-based Variational Inference (ParVI) methods approximate the target distribution by
iteratively evolving finite weighted particle systems. Recent advances of ParVI methods …