4 papers
Detecting Model Misspecification in Bayesian Inverse Problems via Variational Gradient Descent
Qingyang Liu, Matthew A. Fisher, Zheyang Shen +4
Bayesian inference is optimal when the statistical model is well-specified, while outside this setting Bayesian inference can catastrophically fail; accordingly a wealth of post-Ba…
Extrapolation of Tempered Posteriors
Mengxin Xi, Zheyang Shen, Marina Riabiz +2
Tempering is a popular tool in Bayesian computation, being used to transform a posterior distribution into a reference distribution that is more easily approximated. Se…
A Computable Measure of Suboptimality for Entropy-Regularised Variational Objectives
Clémentine Chazal, Heishiro Kanagawa, Zheyang Shen +2
Several methods in statistics and machine learning target a probability distribution for which an entropy-regularised variational objective is minimised. This increased flexibility…
Scalable Monte Carlo for Bayesian Learning
Paul Fearnhead, Christopher Nemeth, Chris J. Oates +1
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.…