bibtype C - Conference Paper (international conference)
ARLID 0431085
utime 20240103204541.9
mtime 20140912235959.9
SCOPUS 84910615503
WOS 000363896100141
title (primary) (eng) Distributed Modelling of Big Dynamic Data with Generalized Linear Models
specification
page_count 8 s.
media_type C
serial
ARLID cav_un_epca*0431084
ISBN 978-84-9012-355-3
title Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
publisher
place Salamanca, Španělsko
name International Society of Information Fusion
year 2014
keyword distributed estimation
keyword big dynamic data
keyword bayesian inference
author (primary)
ARLID cav_un_auth*0242543
name1 Dedecius
name2 Kamil
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0263972
name1 Sečkárová
name2 Vladimíra
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431085.pdf
cas_special
project
ARLID cav_un_auth*0303543
project_id GP14-06678P
agency GA ČR
country CZ
project
ARLID cav_un_auth*0292725
project_id GA13-13502S
agency GA ČR
abstract (eng) The big data, characterized by high volume, velocity and variety, often arise in a dynamic way, requiring fast online processing. This contribution proposes a new information-theoretic method for parallel dynamic statistical modelling of such data with a network of cooperating processing units and an optional fusion center. The concept strongly exploits the principles of the Bayesian information processing, allowing its abstract formulation for arbitrary distributions. As a particular case, we specialize to the popular exponential family posterior distributions, arising either directly or indirectly from modelling with generalized linear models. Still, the applicability is considerably wider.
action
ARLID cav_un_auth*0305592
name 17th International Conference on Information Fusion (Fusion 2014)
dates 07.07.2014-10.07.2014
place Salamanca
country ES
RIV BB
FORD0 10000
FORD1 10100
FORD2 10103
reportyear 2015
num_of_auth 2
presentation_type PR
permalink http://hdl.handle.net/11104/0236066
mrcbC61 1
confidential S
mrcbC83 RIV/67985556:_____/14:00431085!RIV15-GA0-67985556 152500622 Doplnění UT WOS a Scopus
arlyear 2014
mrcbU14 84910615503 SCOPUS
mrcbU34 000363896100141 WOS
mrcbU63 cav_un_epca*0431084 Proceedings of the 17th International Conference on Information Fusion (Fusion 2014) 978-84-9012-355-3 Salamanca, Španělsko International Society of Information Fusion 2014