0474383 20240103214031.920170505235959.9 85020008545 000418403500009 10.1007/978-3-319-54084-9_9 11 s. P cav_un_epca*0474382978-3-319-54084-92194-100991-101ChamSpringer International Publishing2017ArgientoR.LanzaroneE.VillalobosI.MatteiA. Linear inverse problem Variational Bayes inference Convex optimization Uncertain correlations Gamma dose rate measurements Nuclide ratios 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. http://library.utia.cas.cz/separaty/2017/AS/tichy-0474383.pdf cav_un_auth*0318110 7F14287 GA MŠk CZ The choice of regularization for an ill-conditioned linear inverse problem has significant impact on the resulting estimates. We consider a linear inverse model with on the solution in the form of zero mean Gaussian prior and with covariance matrix represented in modified Cholesky form. Elements of the covariance are considered as hyper-parameters with truncated Gaussian prior. The truncation points are obtained from expert judgment as range on correlations of selected elements of the solution. This model is motivated by estimation of mixture of radionuclides from gamma dose rate measurements under the prior knowledge on range of their ratios. Since we aim at high dimensional problems, we use the Variational Bayes inference procedure to derive approximate inference of the model. The method is illustrated and compared on a simple example and on more realistic 6 hours long release of mixture of 3 radionuclides. cav_un_auth*0346112 Bayesian Young Statisticians Meeting 2016 20160619 20160621 Florence IT BB 10000 10100 10103 2018 2 PO RVO:67985556 http://hdl.handle.net/11104/0271455 S 3+4 Proceedings Paper Statistics Probability 3+4 Proceedings Paper Statistics Probability 3+4 Proceedings Paper Statistics Probability 0.217 Q4 2017 85020008545 SCOPUS PUBMED 000418403500009 WOS cav_un_epca*0474382 Bayesian Statistics in Action: BAYSM 2016 978-3-319-54084-9 2194-1009 2194-1017 91 101 Cham Springer International Publishing 2017 Springer Proceedings in Mathematics & Statistics 194 340 Argiento R. 340 Lanzarone E. 340 Villalobos I. 340 Mattei A.