activity
20212025
most citedSwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy

1 citations · 2 across the 6 of their papers we have counts for

collaborators
Showing stat.MLShow all

2 papers · 1 filter

stat.ML2024

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.…

stat.ML2024

Diffusion Generative Modelling for Divide-and-Conquer MCMC

C. Trojan, P. Fearnhead, C. Nemeth

Divide-and-conquer MCMC is a strategy for parallelising Markov Chain Monte Carlo sampling by running independent samplers on disjoint subsets of a dataset and merging their output.…