bibtype A - Abstract
ARLID 0445789
utime 20240103210255.3
mtime 20150806235959.9
title (primary) (eng) Bayesian Estimation of Prior Variance in Source Term Determination
specification
page_count 1 s.
media_type C
serial
ARLID cav_un_epca*0445788
title EGU General Assembly Conference Abstracts
page_num 5563-5563
publisher
place Vienna
name EGU
year 2015
keyword inverse modeling
keyword Bayesian approach
author (primary)
ARLID cav_un_auth*0101207
name1 Šmídl
name2 Václav
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
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*0228606
name1 Hofman
name2 Radek
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
institution UTIA-B
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/2015/AS/smidl-0445789.pdf
cas_special
project
project_id 7F14287
agency GA MŠk
country CZ
ARLID cav_un_auth*0318110
abstract (eng) The classical formulation of the linear inverse problem is studied from Bayesian point of view. We show that the classical regularization is equivalent to prior covariance matrix. We formulate several parametrization of the prior covariance matrix and derive estimation algorithms for them. The advantages of teh new algorithms are demonstrated on data from teh ETEX experiment.
action
ARLID cav_un_auth*0318109
name EGU General Assembly
place Vienna
dates 17.4.2015-22.4.2015
country AT
reportyear 2016
RIV BB
num_of_auth 2
mrcbC52 4 O 4o 20231122141037.2
presentation_type PO
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0248311
confidential S
arlyear 2015
mrcbTft \nSoubory v repozitáři: 0445789.pdf
mrcbU63 cav_un_epca*0445788 EGU General Assembly Conference Abstracts 5563 5563 Vienna EGU 2015