0588431 20250526122429.920240813235959.9 1 s. E cav_un_epca*0597976PrahaCVUT2024 Bayesian methods mathematical modelling neural networks cav_un_auth*0464277 Brožová Antonie UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101207 Šmídl Václav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0267768 Tichý Ondřej UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/AS/brozova-0588431-abstrakt.pdf https://library.utia.cas.cz/separaty/2024/AS/brozova-0588431-prezentace.pdf GA24-10400S GA ČR CZ cav_un_auth*0464279 Recovering a source and an amount of an emitted substance from distant measurement is an ill-posed problem. In this contribution, two methods based on Bayes theorem will be compared on a realistic toy problem with microplastics. First of them is a Bayesian neural network pretrained to mimic a lognormal process and second one is hierarchical variational model, where the parameters of the posterior distribution are modeled by a convolutional neural network. Both these approaches allow to incorporate spatial dependency of the locations of the source and offer an estimate of uncertainty to assess the reliability of the method. cav_un_auth*0471752 Stochastic and Physical Monitoring Systems 2024 (SPMS 2024) /15./ 20240620 20240624 Dobřichovice CZ BC 10000 10200 10201 2025 3 2 O 4 4o 4 20250526122420.7 4 20250526122429.9 PO RVO:67985556 https://hdl.handle.net/11104/0355754 1 S 2024 Soubory v repozitáři: 0588431-abstrakt.pdf SCOPUS PUBMED WOS cav_un_epca*0597976 Stochastic and Physical Monitoring Systems 2024 Praha CVUT 2024