Advanced Bayesian inference with stochastic hydrological models
Beschreibung
With advanced numerical techniques, including Approximate Bayesian Computation (ABC), Hamiltonian Monte Carlo (HMC) and Machine Learning (ML), we want to push the limits of Bayesian inference with stochastic models. The focus of the application will be on the hydrological sciences.
Eckdaten
Projektleitung
Dr. Carlo Albert
Projektteam
Projektpartner
Eidgenössische Anstalt für Wasserversorgung, Abwasserreinigung und Gewässerschutz eawag / Department Systems Analysis, Integrated Assessment and Modelling
Projektstatus
abgeschlossen, 04/2017 - 06/2022
Institut/Zentrum
Institut für Computational Life Sciences (ICLS)
Drittmittelgeber
SNF-Projektförderung / Projekt Nr. 169295
