0445789 20240103210255.320150806235959.9 1 s. C cav_un_epca*04457885563-5563ViennaEGU2015 inverse modeling Bayesian approach cav_un_auth*0101207 Šmídl Václav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0228606 Hofman Radek Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/AS/smidl-0445789.pdf 7F14287 GA MŠk CZ cav_un_auth*0318110 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. cav_un_auth*0318109 EGU General Assembly Vienna 17.4.2015-22.4.2015 AT 2016 BB 2 4 O 4o 20231122141037.2 PO RVO:67985556 http://hdl.handle.net/11104/0248311 S 2015 Soubory v repozitáři: 0445789.pdf cav_un_epca*0445788 EGU General Assembly Conference Abstracts 5563 5563 Vienna EGU 2015