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Hug, S. ; Schmidl, D. ; Li, W.B. ; Greiter, M. ; Theis, F.J.

Bayesian model selection methods and their application to biological ODE systems.

In: Uncertainty in Biology : A Computational Modeling Approach. Berlin ; Heidelberg [u.a.]: Springer, 2016. 243-268 (Stud. Mechanobiol. Tiss. Engineering Biomater. ; 17)
DOI
In this chapter, we focus on Bayesian model selection for biological dynamical systems. We do not present an overview over existing methods, but showcase their comparison and the application to ordinary differential equation (ODE) systems, as well as the inference of the parameters in the ODE system. For this, our method of choice is the Bayes factor, computed by Thermodynamic Integration. We first present several model selection methods, both alternatives to the Bayes factor as well as several methods for calculating the Bayes factor, foremost among them said Thermodynamic Integration. As a simple example for the selection problem, we resort to a choice between normal distributions, which is analytically tractable. We apply our chosen method to a medium sized ODE model selection problem from radiation science and demonstrate how predictions can be drawn from the model selection results.
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Publication type Article: Edited volume or book chapter
Editors Geris, L.* ; Gomez-Cabrero, D.*
Corresponding Author
ISSN (print) / ISBN 1868-2006
Book Volume Title Uncertainty in Biology : A Computational Modeling Approach
Journal Studies in Mechanobiology, Tissue Engineering and Biomaterials
Quellenangaben Volume: 17, Issue: , Pages: 243-268 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin ; Heidelberg [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Bioinformatics and Systems Biology (IBIS)
Research Unit Medical Radiation Physics and Diagnostics (AMSD)