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 …

This is an up-to-date introduction to, and overview of, marginal likelihood computation for
model selection and hypothesis testing. Computing normalizing constants of probability …

A non-parametric extension of control variates is presented. These leverage gradient
information on the sampling density to achieve substantial variance reduction. It is not …

Over forty years ago average-case error was proposed in the applied mathematics literature
as an alternative criterion with which to assess numerical methods. In contrast to worst-case …

A research frontier has emerged in scientific computation, wherein discretisation error is
regarded as a source of epistemic uncertainty that can be modelled. This raises several …

We consider approximate Bayesian model choice for model selection problems that involve
models whose Fisher information matrices may fail to be invertible along other competing …

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 …

Calculation of Bayesian posteriors and model evidences typically requires numerical
integration. Bayesian quadrature (BQ), a surrogate-model-based approach to numerical …

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 …