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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)
Publ. Version/Full Text DOI PMC
Open Access Gold
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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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Biological Sciences ; Mathematical Biosciences ; Systems Biology; Parameter-estimation; Systems
ISSN (print) / ISBN 2589-0042
e-ISSN 2589-0042
Journal iScience
Quellenangaben Volume: 26, Issue: 11, Pages: , Article Number: 108083 Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy
University of Bonn
German Federal Ministry of Education and Research