Causal inference is essential for data-driven decision making across domains such as
business engagement, medical treatment and policy making. However, research on causal …

Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …

Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …

Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-
of-fit tests. It can be applied even when the target distribution has an unknown normalising …

Stein discrepancies have emerged as a powerful tool for retrospective improvement of
Markov chain Monte Carlo output. However, the question of how to design Markov chains …

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

In this study, we consider the weak convergence characteristics of the Integral Probability
Metrics (IPM) methods in training Generative Adversarial Networks (GANs). We first …