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 …

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

Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but
constructing effective control variates can be challenging when the number of samples is …

This paper addresses a large class of nonsmooth nonconvex stochastic DC (difference-of-
convex functions) programs where endogenous uncertainty is involved and iid (independent …

In this paper, we propose a variance reduction approach for Markov chains based on
additive control variates and the minimization of an appropriate estimate for the asymptotic …

Building upon recent developments of force-based estimators with a reduced variance for
the computation of densities, radial distribution functions, or local transport properties from …

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 …

Control variates are variance reduction tools for Monte Carlo estimators. They can provide
significant variance reduction, but usually require a large number of samples, which can be …