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Research Seminar Systems Science

Winter semester 2021/22

Virtual seminar in BigBlueButton (https://webconf.uni-osnabrueck.de/b/fra-h1b-axo-fe5). Entry code available upon request.

Timetable

Date Time Presenter Title Notes
25.10.2021 2:15pm Sonja Türpitz, Friedrich-Schiller-Universität Jena Mutualism, poisoning, and modeling the lung microbiome
16.11.2021 3:00pm Stefan Kittelmann Ein georeferenzierter Modellansatz zur Abschätzung des erosiven Mikroplastikeintrags aus Klärschlamm im Einzugsgebiet der bayerischen Donau (Bachelorarbeit) 66/E01
06.12.2021 2:15pm Dr. Samuel Fischer, Osnabrück University and Helmholtz Centre for Environmental Research KDE-likelihood: a tool for fitting agent-based models to equilibrium data
10.01.2022 2:15pm Friedemann Liebaug Part I
17.01.2022 2:15pm Friedemann Liebaug Part II

Abstracts of selected talks

Samuel Fischer, December 6, 2021

KDE-likelihood: a tool for fitting agent-based models to equilibrium data

Fitting stochastic agent-based models (ABMs) to observational data can be challenging, because the complexity of ABMs typically prohibits direct application of classical statistical tools such as the likelihood. Hence, modellers often examine the parameter space by applying sampling-based methods such as approximate Bayesian computation (ABC), or they reduce stochasticity by aggregating results over many simulation runs. However, if the available data represent a system in equilibrium state, sampling may be computationally costly, since long simulation runs may be required to reach this state. This makes methods such as ABC inapplicable. At the same time, aggregating results may lead to information loss that could result in identifiability issues corrupting the reliability of the parameter estimates. In this talk, we suggest the kernel-density-estimate-based (KDE-) likelihood as a tool circumventing these issues. The KDE-likelihood allows modellers to exploit the favourable statistical properties of the likelihood function without deriving it in closed form. We introduce our approach using a simple ‘toy’ model and showcase the method's advantages in real applications by fitting the forest model Formind to forest inventory data from Changbaishan, China.

Archive: Research Seminars of previous semesters

Archive: Previous Research Group Meetings