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 |
|
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 |
|