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Journal article

Identifiability analysis for stochastic differential equation models in systems biology

Abstract:

Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for determin...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
ORCID:
0000-0002-6304-9333
Publisher:
Royal Society Publisher's website
Journal:
Journal of the Royal Society, Interface Journal website
Publication date:
2020-12-16
Acceptance date:
2020-11-24
DOI:
EISSN:
1742-5662
ISSN:
1742-5689
Pubs id:
1146965
Local pid:
pubs:1146965

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