The Library
Limit theorems for cloning algorithms
Tools
Tools
Tools
Angeli, Letizia, Grosskinsky, Stefan and Johansen, Adam M. (2021) Limit theorems for cloning algorithms. Stochastic Processes and their Applications, 138 . pp. 117-152. doi:10.1016/j.spa.2021.04.007
|
PDF
WRAP-limit-theorems-cloning-algorithms-Johansen-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1944Kb) | Preview |
|
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-limit-theorems-cloning-algorithms-Johansen-2021.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (1008Kb) |
Official URL: https://doi.org/10.1016/j.spa.2021.04.007
Abstract
Large deviations for additive path functionals of stochastic processes have attracted significant research interest, in particular in the context of stochastic particle systems and statistical physics. Efficient numerical ‘cloning’ algorithms have been developed to estimate the scaled cumulant generating function, based on importance sampling via cloning of rare event trajectories. So far, attempts to study the convergence properties of these algorithms in continuous time have led to only partial results for particular cases. Adapting previous results from the literature of particle filters and sequential Monte Carlo methods, we establish a first comprehensive and fully rigorous approach to bound systematic and random errors of cloning algorithms in continuous time. To this end we develop a method to compare different algorithms for particular classes of observables, based on the martingale characterization of stochastic processes. Our results apply to a large class of jump processes on compact state space, and do not involve any time discretization in contrast to previous approaches. This provides a robust and rigorous framework that can also be used to evaluate and improve the efficiency of algorithms.
| Item Type: | Journal Article | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Algorithms, Stochastic processes -- Mathematical models, Limit theorems (Probability theory), Path integrals | ||||||||||||
| Journal or Publication Title: | Stochastic Processes and their Applications | ||||||||||||
| Publisher: | Elsevier Science BV | ||||||||||||
| ISSN: | 0304-4149 | ||||||||||||
| Official Date: | August 2021 | ||||||||||||
| Dates: |
|
||||||||||||
| Volume: | 138 | ||||||||||||
| Page Range: | pp. 117-152 | ||||||||||||
| DOI: | 10.1016/j.spa.2021.04.007 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||
| RIOXX Funder/Project Grant: |
|
||||||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
