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 …

Subsampling focuses on selecting a subsample that can efficiently sketch the information of
the original data in terms of statistical inference. It provides a powerful tool in big data …

Generalised Bayesian inference updates prior beliefs using a loss function, rather than a
likelihood, and can therefore be used to confer robustness against possible mis …

When maximum likelihood estimation is infeasible, one often turns to score matching,
contrastive divergence, or minimum probability flow to obtain tractable parameter estimates …

Among dissimilarities between probability distributions, the Kernel Stein Discrepancy (KSD)
has received much interest recently. We investigate the properties of its Wasserstein …

Stein variational gradient descent (SVGD) is a particle-based inference algorithm that
leverages gradient information for efficient approximate inference. In this work, we enhance …

Decision making under uncertainty is critical to real-world, autonomous systems. Model
Predictive Control (MPC) methods have demonstrated favorable performance in practice …

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 …

We introduce kernel thinning, a new procedure for compressing a distribution $\mathbb {P} $
more effectively than iid sampling or standard thinning. Given a suitable reproducing kernel …

Stein discrepancies (SDs) monitor convergence and non-convergence in approximate
inference when exact integration and sampling are intractable. However, the computation of …