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 …

Gradient estimation---approximating the gradient of an expectation with respect to the
parameters of a distribution---is central to the solution of many machine learning problems …

Regularized Zero-Variance Control Variates Page 1 Bayesian Analysis (2023) 18, Number 3,
pp. 865–888 Regularized Zero-Variance Control Variates ∗ LF South †,‡ , CJ Oates § , A. Mira …

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

Bayesian probabilistic numerical methods for numerical integration offer significant
advantages over their non-Bayesian counterparts: they can encode prior information about …

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 …

A novel control variate technique is proposed for the post-processing of Markov chain Monte
Carlo output, based on both Stein's method and an approach to numerical integration due to …

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 …