bibtype C - Conference Paper (international conference)
ARLID 0525233
utime 20240103224203.6
mtime 20200623235959.9
SCOPUS 85090153735
WOS 000613138000007
DOI 10.23919/ECC51009.2020.9143856
title (primary) (eng) Minimum Expected Relative Entropy Principle
specification
page_count 6 s.
media_type C
serial
ARLID cav_un_epca*0524379
ISBN 978-390714401-5
title Proceedings of the 18th European Control Conference (ECC)
page_num 35-40
publisher
place Saint Petersburg
name European Union Control Association (EUCA)
year 2020
keyword minimum relative entropy principle
keyword uncertain prior probability
keyword forgetting
keyword fully probabilistic design
keyword abrupt parameter changes
author (primary)
ARLID cav_un_auth*0101124
name1 Kárný
name2 Miroslav
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
garant K
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2020/AS/karny-0525233.pdf
cas_special
project
project_id LTC18075
agency GA MŠk
country CZ
ARLID cav_un_auth*0372050
project
project_id CA16228
agency The European Cooperation in Science and Technology (COST)
country XE
ARLID cav_un_auth*0372051
abstract (eng) Stochastic filtering estimates a timevarying (multivariate) parameter (a hidden variable) from noisy observations. It needs both observation and parameter evolution models. The latter is often missing or makes the estimation too complex. Then, the axiomatic minimum relative entropy (MRE) principle completes the posterior probability density (pd) of the parameter. The MRE principle recommends to modify a prior guess of the constructed pd to the smallest extent enforced by new observations. The MRE principle does not deal with a generic uncertain prior guess. Such uncertainty arises, for instance, when the MRE principle is used recursively. The paper fills this gap. The proposed minimum expected relative entropy (MeRE) principle: (a) makes Bayesian estimation less sensitive to the choice of the prior pd. (b) provides a stabilised parameter tracking with a data-dependent forgetting that copes with abrupt parameter changes. (c) applies in all cases exploiting MRE, for instance, in stochastic modelling.
action
ARLID cav_un_auth*0392145
name The European Control Conference (ECC 2020)
dates 20200512
mrcbC20-s 20200515
place Saint Petersburg
country RU
RIV BC
FORD0 10000
FORD1 10200
FORD2 10201
reportyear 2021
mrcbC52 4 A sml 4as 20231122145000.9
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0309415
confidential S
contract
name Transfer of Copyright Agreement
date 20200214
mrcbC86 n.a. Proceedings Paper Automation Control Systems|Engineering Electrical Electronic|Operations Research Management Science|Mathematics Applied
arlyear 2020
mrcbTft \nSoubory v repozitáři: karny-0525233-CopyrightForm_426.pdf
mrcbU14 85090153735 SCOPUS
mrcbU24 PUBMED
mrcbU34 000613138000007 WOS
mrcbU63 cav_un_epca*0524379 Proceedings of the 18th European Control Conference (ECC) 978-390714401-5 35 40 Saint Petersburg European Union Control Association (EUCA) 2020