| 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 |
|