A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces

Understand

Kernel Stein discrepancy (KSD) is a widely used kernel-based measure of discrepancy between probability measures.

• It is often employed in the scenario where a user has a collection of samples from a candidate probability measure and wishes to compare them against a specified target probability measure.
• KSD has been employed in a range of settings including goodness-of-fit testing, parametric inference, MCMC output assessment and generative modelling.
• However, so far the method has been restricted to finite-dimensional data.