0489184 20240103215939.620180426235959.9 1 s. E Vol. 20MunichEGU2018 linear inverse problem non-Gaussian statistics ETEX cav_un_auth*0267768 Tichý Ondřej 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*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*0352443 Hýža M. CZ cav_un_auth*0360250 Šindelářová Kateřina CZ cav_un_auth*0341005 Ulrych Lukáš Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0335167 Belal Alkomiet Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2018/AS/tichy-0489184.pdf In this contribution, we propose two probabilistic models of the residue statistics. The first model is based on the assumption of non-Gaussian statistics of the residue. In particular, we choose Student-t model of the residues and design a Variational Bayesian estimation procedure for the source term. The second model is based on Gaussian model of the residues with non-diagonal covariance matrix. cav_un_auth*0360251 EGU General Assembly 2018 20180409 20180413 Vídeň AT BB 10000 10100 10103 2019 6 4 O 4o 20231122143142.9 PO RVO:67985556 http://hdl.handle.net/11104/0283742 1 cav_un_auth*0360252 Státní ústav radiační ochrany, v. v. i. CZ cav_un_auth*0360253 Matematicko-fyzikální fakulta, UK CZ S 2018 Soubory v repozitáři: 0489184.pdf SCOPUS PUBMED WOS Geophysical Research Abstracts Vol. 20 EGU2018-15902 Munich EGU 2018