1 citations · 1 across the 3 of their papers we have counts for
4 papers
MCMC for State Space models
Paul Fearnhead, Chris Sherlock
A state-space model is a time-series model that has an unobserved latent process from which we take noisy measurements over time. The observations are conditionally independent giv…
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.…
Metropolis--Hastings with Scalable Subsampling
Estevão Prado, Christopher Nemeth, Chris Sherlock
The Metropolis-Hastings (MH) algorithm is one of the most widely used Markov Chain Monte Carlo schemes for generating samples from Bayesian posterior distributions. The algorithm i…
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy
Callum Vyner, Christopher Nemeth, Chris Sherlock
Divide-and-conquer strategies for Monte Carlo algorithms are an increasingly popular approach to making Bayesian inference scalable to large data sets. In its simplest form, the da…