bibtype J - Journal Article
ARLID 0597140
utime 20240909092456.2
mtime 20240820235959.9
SCOPUS 85199786976
WOS 001267278400001
DOI 10.1214/24-EJP1161
title (primary) (eng) Weaves, webs and flows
specification
page_count 82 s.
media_type E
serial
ARLID cav_un_epca*0041954
ISSN 1083-6489
title Electronic Journal of Probability
volume_id 29
volume 1 (2024)
page_num 1-82
publisher
name Institute of Mathematical Statistics
keyword flow
keyword weave
keyword web
author (primary)
ARLID cav_un_auth*0471433
name1 Freeman
name2 N.
country GB
share 50
author
ARLID cav_un_auth*0217893
name1 Swart
name2 Jan M.
institution UTIA-B
full_dept (cz) Stochastická informatika
full_dept Department of Stochastic Informatics
department (cz) SI
department SI
full_dept Department of Stochastic Informatics
country CZ
share 50
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url https://library.utia.cas.cz/separaty/2024/SI/swart-0597140.pdf
cas_special
project
project_id GA22-12790S
agency GA ČR
country CZ
ARLID cav_un_auth*0449240
abstract (eng) We introduce weaves, which are random sets of non-crossing càdlàg paths that cover space-time R × R. The Brownian web is one example of a weave, but a key feature of our work is that we do not assume that the particle motions have any particular distribution. Rather, we present a general theory of the structure, characterization and weak convergence of weaves. We show that the space of weaves has an appealing geometry, involving a partition into equivalence classes under which each equivalence class contains a pair of distinguished objects known as a web and a flow. Webs are natural generalizations of the Brownian web and the flows provide pathwise representations of stochastic flows. Moreover, there is a natural partial order on the space of weaves, characterizing the efficiency with which paths cover space-time, under which webs are precisely minimal weaves and flows are precisely maximal weaves. This structure is key to establishing weak convergence criteria for general weaves, based on weak convergence of finite collections of particle motions.
result_subspec WOS
RIV BA
FORD0 10000
FORD1 10100
FORD2 10103
reportyear 2025
num_of_auth 2
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0355755
cooperation
ARLID cav_un_auth*0332435
name University of Sheffield
country GB
confidential S
mrcbC91 A
mrcbT16-e STATISTICSPROBABILITY
mrcbT16-j 1.284
mrcbT16-D Q2
arlyear 2024
mrcbU14 85199786976 SCOPUS
mrcbU24 PUBMED
mrcbU34 001267278400001 WOS
mrcbU63 cav_un_epca*0041954 Electronic Journal of Probability 29 1 2024 1 82 1083-6489 1083-6489 Institute of Mathematical Statistics