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 propose a goodness-of-fit measure for probability densities modeling observations with
varying dimensionality, such as text documents of differing lengths or variable-length …

We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy
(KSD). We introduce a strategy to construct a test, called KSDAgg, which aggregates …

Model misspecification can create significant challenges for the implementation of
probabilistic models, and this has led to development of a range of robust methods which …

Kernel-based tests provide a simple yet effective framework that uses the theory of
reproducing kernel Hilbert spaces to design non-parametric testing procedures. In this …

We propose and analyse a novel nonparametric goodness-of-fit testing procedure for ex-
changeable exponential random graph model (ERGM) when a single network realisation is …

We explore the minimax optimality of goodness-of-fit tests on general domains using the
kernelized Stein discrepancy (KSD). The KSD framework offers a flexible approach for …

Stein discrepancies have emerged as a powerful statistical tool, being applied to
fundamental statistical problems including parameter inference, goodness-of-fit testing, and …

In many applications, we encounter data on Riemannian manifolds such as torus and
rotation groups. Standard statistical procedures for multivariate data are not applicable to …

Goodness-of-fit testing is often criticized for its lack of practical relevance; since``all models
are wrong'', the null hypothesis that the data conform to our model is ultimately always …