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 …

The use of heuristics to assess the convergence and compress the output of Markov chain
Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced …

This paper proposes and studies a numerical method for approximation of posterior
expectations based on interpolation with a Stein reproducing kernel. Finite-sample-size …

Several researchers have proposed minimisation of maximum mean discrepancy (MMD) as
a method to quantise probability measures, ie, to approximate a distribution by a …

A complete recipe of measure-preserving diffusions in Euclidean space was recently
derived unifying several MCMC algorithms into a single framework. In this paper, we …

We use Stein characterizations to obtain new moment-type estimators for the parameters of
three classical spherical distributions (namely the Fisher-Bingham, the von Mises-Fisher …

In many applications, we encounter data on Riemannian manifolds such as torus and
rotation groups. Standard statistical procedures for multivariate data are not applicable to …

We use Stein characterisations to derive new moment-type estimators for the parameters of
several multivariate distributions in the iid case; we also derive the asymptotic properties of …

We prove a central limit theorem (CLT) for the Fréchet mean of independent and identically
distributed observations in a compact Riemannian manifold assuming that the population …

Non-parametric goodness-of-fit testing procedures based on kernel Stein discrepancies
(KSD) are promising approaches to validate general unnormalised distributions in various …