Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains

Journal Article (2018)
Author(s)

Joris Bierkens (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Alexandre Bouchard-Côté (University of British Columbia)

Arnaud Doucet (University of Oxford)

Andrew B. Duncan (University of Sussex)

Paul Fearnhead (University of Lancaster)

Thibaut Lienart (University of Oxford)

Gareth Roberts (University of Warwick)

Sebastian J. Vollmer (University of Warwick)

Research Group
Statistics
DOI related publication
https://doi.org/10.1016/j.spl.2018.02.021 Final published version
More Info
expand_more
Publication Year
2018
Language
English
Research Group
Statistics
Volume number
136
Pages (from-to)
148-154
Downloads counter
373
Collections
Institutional Repository
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how they can be implemented in settings where the parameters live on a restricted domain.

Files

Manuscript.pdf
(pdf | 0.664 Mb)
- Embargo expired in 25-05-2020