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 …

Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to
approximate the posterior and derived quantities of interest. Despite this, the issue of how …

Summary Markov chain Monte Carlo (MCMC) methods provide consistent approximations of
integrals as the number of iterations goes to∞. MCMC estimators are generally biased after …

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

Stein importance sampling is a widely applicable technique based on kernelized Stein
discrepancy, which corrects the output of approximate sampling algorithms by reweighting …

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 …

Control variates are a well-established tool to reduce the variance of Monte Carlo
estimators. However, for large-scale problems including high-dimensional and large-sample …

In many fields, data appears in the form of direction (unit vector) and usual statistical
procedures are not applicable to such directional data. In this study, we propose …

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 introduce a general framework that constructs estimators with reduced variance for
random walk Metropolis and Metropolis-adjusted Langevin algorithms. The resulting …