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 …

We derive a new discrepancy statistic for measuring differences between two probability
distributions based on combining Stein's identity and the reproducing kernel Hilbert space …

Stein variational gradient descent (SVGD) is a deterministic sampling algorithm that
iteratively transports a set of particles to approximate given distributions, based on a …

Abstract Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid
sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to …

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 …

To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased
Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational …

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 …

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 …

An important task in computational statistics and machine learning is to approximate a
posterior distribution $ p (x) $ with an empirical measure supported on a set of …

Policy gradient methods have achieved remarkable successes in solving challenging
reinforcement learning problems. However, it still often suffers from the large variance issue …