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 series of computationally efficient, nonparametric tests for the two-sample,
independence and goodness-of-fit problems, using the Maximum Mean Discrepancy …

Simulator-based models are models for which the likelihood is intractable but simulation of
synthetic data is possible. They are often used to describe complex real-world phenomena …

Likelihood-free inference methods typically make use of a distance between simulated and
real data. A common example is the maximum mean discrepancy (MMD), which has …

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 …

Single-cell technologies allow us to gain insights into cellular processes at unprecedented
resolution. In stem cell and developmental biology snapshot data allow us to characterize …

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 …

For two decades, reproducing kernels and their associated discrepancies have facilitated
elegant theoretical analyses in the setting of quasi Monte Carlo. These same tools are now …

Unnormalized statistical models play an important role in machine learning, statistics, and
signal processing. In this paper, we derive a new hypothesis testing procedure for …

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 …