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 |
|