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Uncertainty analysis for non-identifiable dynamical systems: Profile likelihoods, bootstrapping and more.
Lect. Notes Comput. Sc. 8859, 61-72 (2014)
Dynamical systems are widely used to describe the behaviour of biological systems. When estimating parameters of dynamical systems, noise and limited availability of measurements can lead to uncertainties. These uncertainties have to be studied to understand the limitations and the predictive power of a model. Several methods for uncertainty analysis are available. In this paper we analysed and compared bootstrapping, profile likelihood, Fisher information matrix, and multi-start based approaches for uncertainty analysis. The analysis was carried out on two models which contain structurally non-identifiable parameters. We showed that bootstrapping, multi-start optimisation, and Fisher information matrix based approaches yield misleading results for parameters which are structurally non-identifiable. We provide a simple and intuitive explanation for this, using geometric arguments.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Herausgeber
Mendes, P.* ; Dada, J.O.* ; Smallbone, K.*
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
ISBN
978-3-319-12981-5
Konferenztitel
Computational Methods in Systems Biology
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 8859,
Seiten: 61-72
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Institute of Computational Biology (ICB)