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A Comparative Analysis of Classical and Approximate Bayesian Inference Techniques in Multiparameter Compartmental Epidemiological Models for COVID-19 Pandemic

Bazarova, Alina and Jadebeck, Johann F. and Nöh, Katharina and Wiechert, Wolfgang and Kühn, Martin Joachim and Kesselheim, Stefan (2024) A Comparative Analysis of Classical and Approximate Bayesian Inference Techniques in Multiparameter Compartmental Epidemiological Models for COVID-19 Pandemic. Helmholtz AI Conference 2024, 2024-06-12, Düsseldorf.

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Item URL in elib:https://elib.dlr.de/204876/
Document Type:Conference or Workshop Item (Poster)
Title:A Comparative Analysis of Classical and Approximate Bayesian Inference Techniques in Multiparameter Compartmental Epidemiological Models for COVID-19 Pandemic
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bazarova, AlinaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Jadebeck, Johann F.Institute for Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbHUNSPECIFIEDUNSPECIFIED
Nöh, KatharinaInstitute for Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbHUNSPECIFIEDUNSPECIFIED
Wiechert, WolfgangInstitute for Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbHUNSPECIFIEDUNSPECIFIED
Kühn, Martin JoachimUNSPECIFIEDhttps://orcid.org/0000-0002-0906-6984UNSPECIFIED
Kesselheim, StefanJülich Supercomputing CentreUNSPECIFIEDUNSPECIFIED
Date:12 June 2024
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Parameter estimation, MCMC, Normalizing flows, Artificial intelligence, Machine learning, Covid-19
Event Title:Helmholtz AI Conference 2024
Event Location:Düsseldorf
Event Type:national Conference
Event Date:12 June 2024
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Tasks SISTEC
Location: Köln-Porz
Institutes and Institutions:Institute of Software Technology > High-Performance Computing
Institute of Software Technology
Deposited By: Kühn, Dr. Martin Joachim
Deposited On:27 Jun 2024 13:25
Last Modified:27 Jun 2024 13:25

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