0448581 20240103210837.220151022235959.9 1 s. P cav_un_epca*0448580142-142ViennaCTBTO2015 Bayesian analysis nonsupervised analysis parameterized priors 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. 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*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. http://library.utia.cas.cz/separaty/2015/AS/smidl-0448581.pdf 7F14287 GA MŠk CZ cav_un_auth*0318110 Tools for the fusion of atmospheric transport models with data are of a great importance in many fields where characteristics of a source term are sought. The most promising tools seem to be those based on Bayesian analysis, with the most appealing feature being the inherent capability to treat the full probability distribution of involved uncertainties. Since the output is a posterior probability distribution, probabilistic interpretation of results can be drawn. However, practical application of Bayesian methods can be difficult because a proper specification of the a prior distribution is needed. The tools must ensure that the prior selection procedure is robust enough to work under various circumstances, particularly in the case of continuously operating nonsupervised analysis. A solution to this problem can be the use of simple parameterized priors. Here, we present a method based on prior hyper-parametrization and estimation of these parameters using Variational Bayes method. cav_un_auth*0320636 CTBT: Science and Technology 2015 Vienna 22.06.2015-26.06.2015 AT 2016 BB 3 4 O 4o 20231122141215.2 PO RVO:67985556 http://hdl.handle.net/11104/0250584 S 2015 Soubory v repozitáři: 0448581.pdf cav_un_epca*0448580 CTBT: Science and Technology 2015 142 142 Vienna CTBTO 2015