bibtype |
J -
Journal Article
|
ARLID |
0411493 |
utime |
20240103182331.9 |
mtime |
20060210235959.9 |
title
(primary) (eng) |
Prior knowledge in estimation of bi-phase model of 131I accumulation in thyroid gland. Abstract |
specification |
|
serial |
ARLID |
cav_un_epca*0257858 |
ISSN |
1619-7070 |
title
|
European Journal of Nuclear Medicine and Molecular Imaging |
volume_id |
32 |
page_num |
42 |
publisher |
|
|
title
(cze) |
Apriorní informace v odhadování parametrů dvojfázového modelu akumulace 131I ve štítné žláze |
keyword |
Bayesian identification |
keyword |
linear regression model |
keyword |
prior constraint of parameters |
author
(primary) |
ARLID |
cav_un_auth*0202818 |
name1 |
Varga |
name2 |
F. |
country |
CZ |
|
author
|
ARLID |
cav_un_auth*0101119 |
name1 |
Jirsa |
name2 |
Ladislav |
institution |
UTIA-B |
full_dept |
Department of Adaptive Systems |
fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
author
|
ARLID |
cav_un_auth*0015563 |
name1 |
Heřmanská |
name2 |
J. |
country |
CZ |
|
author
|
ARLID |
cav_un_auth*0101124 |
name1 |
Kárný |
name2 |
Miroslav |
institution |
UTIA-B |
full_dept |
Department of Adaptive Systems |
fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
author
|
ARLID |
cav_un_auth*0000043 |
name1 |
Vlček |
name2 |
P. |
country |
CZ |
|
source |
|
COSATI |
06R |
COSATI |
12B |
cas_special |
project |
project_id |
1ET100750404 |
agency |
GA AV ČR |
ARLID |
cav_un_auth*0001793 |
|
project |
project_id |
IBS1075351 |
agency |
GA AV ČR |
ARLID |
cav_un_auth*0001804 |
|
research |
CEZ:AV0Z10750506 |
abstract
(eng) |
Identification of a bi-phase model of activity accumulation in thyroid gland is enhanced by prior information on uncertain parameters. The improvement is tested on the prediction abilities of the model. The methodology is suitable for the considered case when a little amount of noisy data is available for the identificaiton. |
abstract
(cze) |
Identifikace dvojfázového modelu akumulace aktivity ve štítné žláze je zlepšeno apriorní informací o neznámých parametrech. Zlepšení je testováno na predikčních schopnostech modelu. Použitá metodologie je vhodná pro uvažovaný případ, kdy je pro indentifikaci dostupné malé množství zašuměných dat. |
RIV |
BB |
reportyear |
2006 |
department |
AS |
permalink |
http://hdl.handle.net/11104/0131573 |
ID_orig |
UTIA-B 20050223 |
arlyear |
2005 |
mrcbU63 |
cav_un_epca*0257858 European Journal of Nuclear Medicine and Molecular Imaging 1619-7070 1619-7089 Roč. 32 Suppl. 1 2005 42 Springer |
|