The Library
Quasi-stationary Monte Carlo and the ScaLE Algorithm
Tools
Pollock, Murray, Fearnhead, Paul, Johansen, Adam M. and Roberts, Gareth O. (2020) Quasi-stationary Monte Carlo and the ScaLE Algorithm. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82 (5). pp. 1167-1221. doi:10.1111/rssb.12365 ISSN 1369-7412.
|
PDF
WRAP-Quasi-stationary-Monte-Carlo-ScaLE-Algorithm-Roberts-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3972Kb) | Preview |
|
PDF
WRAP-quasi-stationary-monte-carlo-scale-algorithm-Roberts-2020.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (7Mb) |
Official URL: https://doi.org/10.1111/rssb.12365
Abstract
This paper introduces a class of Monte Carlo algorithms which are based upon the simulation of a Markov process whose quasi-stationary distribution coincides with a distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo methods which simulate a Markov chain whose stationary distribution is the target. We show how to approximate distributions of interest by carefully combining sequential Monte Carlo methods with methodology for the exact simulation of diffusions. The methodology introduced here is particularly promising in that it is applicable to the same class of problems as gradient based Markov chain Monte Carlo algorithms but entirely circumvents the need to conduct Metropolis-Hastings type accept/reject steps whilst retaining exactness: the paper gives theoretical guarantees ensuring the algorithm has the correct limiting target distribution. Furthermore, this methodology is highly amenable to big data problems. By employing a modification to existing na¨ıve sub-sampling and control variate techniques it is possible to obtain an algorithm which is still exact but has sub-linear iterative cost as a function of data size.
Item Type: | Journal Article | |||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics | |||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Monte Carlo method , Sampling (Statistics), Brownian motion processes, Langevin equations, Markov processes | |||||||||||||||||||||
Journal or Publication Title: | Journal of the Royal Statistical Society: Series B (Statistical Methodology) | |||||||||||||||||||||
Publisher: | Wiley | |||||||||||||||||||||
ISSN: | 1369-7412 | |||||||||||||||||||||
Official Date: | December 2020 | |||||||||||||||||||||
Dates: |
|
|||||||||||||||||||||
Volume: | 82 | |||||||||||||||||||||
Number: | 5 | |||||||||||||||||||||
Page Range: | pp. 1167-1221 | |||||||||||||||||||||
DOI: | 10.1111/rssb.12365 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||
Date of first compliant deposit: | 19 February 2020 | |||||||||||||||||||||
Date of first compliant Open Access: | 10 November 2020 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
|
|||||||||||||||||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year