| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0461646 |
| utime |
20240103212459.8 |
| mtime |
20160811235959.9 |
| SCOPUS |
84987915625 |
| WOS |
000390840200071 |
| DOI |
10.1109/SSP.2016.7551775 |
| title
(primary) (eng) |
Diffusion estimation of mixture models with local and global parameters |
| specification |
| page_count |
5 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0461645 |
| ISBN |
978-1-4673-7802-4 |
| title
|
Proceedings of the 2016 IEEE Workshop on Statistical Signal Processing |
| page_num |
362-366 |
| publisher |
| place |
Palma de Mallorca, Španělsko |
| name |
IEEE |
| year |
2016 |
|
|
| keyword |
diffusion estimation |
| keyword |
distributed estimation |
| keyword |
exponential family |
| author
(primary) |
| ARLID |
cav_un_auth*0242543 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Dedecius |
| name2 |
Kamil |
| institution |
UTIA-B |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0263972 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Sečkárová |
| name2 |
Vladimíra |
| institution |
UTIA-B |
| country |
CZ |
| 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 state-of-art methods for distributed estimation of mixtures assume the existence of a common mixture model. In many practical situations, this assumption may be too restrictive, as a subset of parameters may be purely local, e.g., if the numbers of observable components differ across the network. To reflect this issue, we propose a new online Bayesian method for simultaneous estimation of local parameters, and diffusion estimation of global parameters. The algorithm consists of two steps. First, the nodes perform local estimation from own observations by means of factorized prior/posterior distributions. Second, a diffusion optimization step is used to merge the nodes' global parameters estimates. A simulation example demonstrates improved performance in estimation of both parameters sets. |
| action |
| ARLID |
cav_un_auth*0332142 |
| name |
2016 IEEE Statistical Signal Processing Workshop |
| dates |
26.06.2016-29.06.2016 |
| place |
Palma de Mallorca |
| country |
ES |
|
| RIV |
BD |
| reportyear |
2017 |
| num_of_auth |
2 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0261345 |
| confidential |
S |
| mrcbC86 |
n.a. Proceedings Paper Engineering Electrical Electronic|Telecommunications |
| arlyear |
2016 |
| mrcbU14 |
84987915625 SCOPUS |
| mrcbU34 |
000390840200071 WOS |
| mrcbU63 |
cav_un_epca*0461645 Proceedings of the 2016 IEEE Workshop on Statistical Signal Processing 978-1-4673-7802-4 362 366 Palma de Mallorca, Španělsko IEEE 2016 |
|