1 citations · 1 across the 3 of their papers we have counts for
3 papers · 1 filter
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.…
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.…
Spatial Latent Gaussian Modelling with Change of Support
Erick A. Chacón-Montalván, Peter M. Atkinson, Christopher Nemeth +2
Spatial data are often derived from multiple sources (e.g. satellites, in-situ sensors, survey samples) with different supports, but associated with the same properties of a spatia…