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Raimundez, E.* ; Fedders, M.* ; Hasenauer, J.

Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes.

iScience 26:108083 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter and prediction uncertainties. Yet, generating representative samples from the posterior distribution is often computationally challenging. Here, we present an approach that lowers the computational complexity of sample generation for dynamical models with scaling, offset, and noise parameters. The proposed method is based on the marginalization of the posterior distribution. We provide analytical results for a broad class of problems with conjugate priors and show that the method is suitable for a large number of applications. Subsequently, we demonstrate the benefit of the approach for applications from the field of systems biology. We report an improvement up to 50 times in the effective sample size per unit of time. As the scheme is broadly applicable, it will facilitate Bayesian inference in different research fields.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Biological Sciences ; Mathematical Biosciences ; Systems Biology; Parameter-estimation; Systems
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2589-0042
e-ISSN 2589-0042
Zeitschrift iScience
Quellenangaben Band: 26, Heft: 11, Seiten: , Artikelnummer: 108083 Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-553800-001
Förderungen Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy
University of Bonn
German Federal Ministry of Education and Research
DOI 10.1016/j.isci.2023.108083
WOS ID 001112947400001
Scopus ID 85173956776
PubMed ID 37867942
Erfassungsdatum 2023-11-28
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