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 |
|
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 |
|