6 papers
A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK
Thomas Pinder, Michael Hollaway, Christopher Nemeth +2
In March 2020 the United Kingdom (UK) entered a nationwide lockdown period due to the Covid-19 pandemic. As a result, levels of nitrogen dioxide (NO2) in the atmosphere dropped. In…
Discussion of "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé
Leah F. South, Chris Nemeth, Chris J. Oates
This is a contribution for the discussion on "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé to appear in the Journal of the…
Stochastic gradient Markov chain Monte Carlo
Christopher Nemeth, Paul Fearnhead
Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually sim…
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
Jamie Fairbrother, Christopher Nemeth, Maxime Rischard +2
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Ga…
Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker, Paul Fearnhead, Emily B Fox +1
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popular method for scalable Bayesian inference. These methods are based on sampling a discrete-time approximation…
Control Variates for Stochastic Gradient MCMC
Jack Baker, Paul Fearnhead, Emily B. Fox +1
It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size. A popular class of methods for solving this issue is stochastic gradient MCMC. These m…