Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts
that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution …

This chapter surveys progress on three related topics in perturbations of Markov chains: the
motivating question of when and how" perturbed" MCMC chains are developed, the …

It is widely known that the performance of Markov chain Monte Carlo (MCMC) can degrade
quickly when targeting computationally expensive posterior distributions, such as when the …

In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm
tailored for solving a certain class of non-convex distributionally robust optimisation …

Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by
data subsampling has recently received considerable attention. A pseudo-marginal MCMC …

Scientists continue to develop increasingly complex mechanistic models to reflect their
knowledge more realistically. Statistical inference using these models can be challenging …

Delayed-acceptance is a technique for reducing computational effort for Bayesian models
with expensive likelihoods. Using a delayed-acceptance kernel for Markov chain Monte …

We provide a simple and general solution to the fundamental open problem of inaccurate
uncertainty quantification of Bayesian inference in misspecified or approximate models, and …

A multivariate generalisation of the Whittle likelihood is used to extend spectral subsampling
MCMC to stationary multivariate time series by subsampling matrix-valued periodogram …

Abstract Speeding up Markov chain Monte Carlo (MCMC) for datasets with many
observations by data subsampling has recently received considerable attention. A pseudo …