2 code implementations • ICML 2020 • Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani, Gareth Roberts
We demonstrate theoretically and empirically that we can also construct a control-variate subsampling boomerang sampler which is also exact, and which possesses remarkable scaling properties in the large data limit.
3 code implementations • 16 Jan 2020 • Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer
We introduce the use of the Zig-Zag sampler to the problem of sampling conditional diffusion processes (diffusion bridges).
Statistics Theory Probability Methodology Statistics Theory
4 code implementations • 16 Jan 2017 • Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet, Andrew B. Duncan, Paul Fearnhead, Thibaut Lienart, Gareth Roberts, Sebastian J. Vollmer
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration.
Methodology Computation
no code implementations • 23 Nov 2016 • Paul Fearnhead, Joris Bierkens, Murray Pollock, Gareth O. Roberts
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-time, rather than discrete-time, Markov processes.
6 code implementations • 11 Jul 2016 • 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.
Computation Probability 65C60, 65C05, 62F15, 60J25
