Speaker
Antonie Brožová
Description
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.
Primary authors
Antonie Brožová
Prof.
Václav Šmídl
(UTIA, CAS)
Dr
Ondřej Tichý
(UTIA, CAS)
