Abstract Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold
standard technique for Bayesian inference. They are theoretically well-understood and …

Nonlinear mixed effects models have become a standard platform for analysis when data is
in the form of continuous and repeated measurements of subjects from a population of …

Deep learning is gaining increasing popularity for spatiotemporal forecasting. However,
prior works have mostly focused on point estimates without quantifying the uncertainty of the …

The identification of nonlinear systems is a challenging problem. Physical knowledge of a
system can be used in the identification process to significantly improve the predictive …

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

This book aims to provide a graduate-level introduction to advanced topics in Markov chain
Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context …

Divide-and-conquer Bayesian methods consist of three steps: dividing the data into smaller
computationally manageable subsets, running a sampling algorithm in parallel on all the …

State space models (SSMs) provide a flexible framework for modeling complex time series
via a latent stochastic process. Inference for nonlinear, non-Gaussian SSMs is often tackled …

In this work, we develop a constructive modeling framework for extreme threshold
exceedances in repeated observations of spatial fields, based on general product mixtures …

Bayesian computational algorithms tend to scale poorly as data size increases. This has
motivated divide-and-conquer-based approaches for scalable inference. These divide the …