| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0431804 |
| utime |
20240103204632.5 |
| mtime |
20141021235959.9 |
| SCOPUS |
84912570469 |
| WOS |
000393407800075 |
| DOI |
10.1109/MLSP.2014.6958920 |
| title
(primary) (eng) |
Diffusion Estimation Of State-Space Models: Bayesian Formulation |
| specification |
| page_count |
6 s. |
| media_type |
C |
|
| serial |
| ARLID |
cav_un_epca*0431803 |
| ISBN |
978-1-4799-3693-9 |
| title
|
Proceedings of the 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP2014) |
| publisher |
| place |
Reims |
| name |
IEEE |
| year |
2014 |
|
|
| keyword |
distributed estimation |
| keyword |
state-space models |
| keyword |
Bayesian estimation |
| 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. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0303543 |
| project_id |
GP14-06678P |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
The paper studies the problem of decentralized distributed estimation of the state-space models from the Bayesian viewpoint. The adopted diffusion strategy, consisting of collective adaptation to new data and combination of posterior estimates, is derived in general model-independent form. Its particular application to the celebrated Kalman filter demonstrates the ease of use, especially when the measurement model is rewritten into the exponential family form and a conjugate prior describes the estimated state. |
| action |
| ARLID |
cav_un_auth*0306303 |
| name |
The 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP2014) |
| dates |
21.09.2014-24.09.2014 |
| place |
Reims |
| country |
FR |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2015 |
| num_of_auth |
1 |
| presentation_type |
PO |
| permalink |
http://hdl.handle.net/11104/0237640 |
| mrcbC61 |
1 |
| confidential |
S |
| mrcbC83 |
RIV/67985556:_____/14:00431804!RIV15-GA0-67985556 152501063 Doplnění UT WOS a Scopus |
| arlyear |
2014 |
| mrcbU14 |
84912570469 SCOPUS |
| mrcbU34 |
000393407800075 WOS |
| mrcbU63 |
cav_un_epca*0431803 Proceedings of the 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP2014) 978-1-4799-3693-9 Reims IEEE 2014 |
|