139 citations · 616 across the 32 of their papers we have counts for
3 papers · 1 filter
Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo
Paul Fearnhead, Joris Bierkens, Murray Pollock +1
Recently there have been exciting developments in Monte Carlo methods, with the development of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-ti…
Limit theorems for the Zig-Zag process
Joris Bierkens, Andrew Duncan
Markov chain Monte Carlo methods provide an essential tool in statistics for sampling from complex probability distributions. While the standard approach to MCMC involves construct…
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data
Joris Bierkens, Paul Fearnhead, Gareth Roberts
Standard MCMC methods can scale poorly to big data settings due to the need to evaluate the likelihood at each iteration. There have been a number of approximate MCMC algorithms th…