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
url http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431804.pdf
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