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Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation.

Bioinformatics 36, 1, 551-559 (2020)
Verlagsversion DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Motivation: Approximate Bayesian computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, as it allows analyzing complex stochastic models. However, the introduced approximation error is often not clear. It has been shown that ABC actually gives exact inference under the implicit assumption of a measurement noise model. Noise being common in biological systems, it is intriguing to exploit this insight. But this is difficult in practice, as ABC is in general highly computationally demanding. Thus, the question we want to answer here is how to efficiently account for measurement noise in ABC.Results: We illustrate exemplarily how ABC yields erroneous parameter estimates when neglecting measurement noise. Then, we discuss practical ways of correctly including the measurement noise in the analysis. We present an efficient adaptive sequential importance sampling-based algorithm applicable to various model types and noise models. We test and compare it on several models, including ordinary and stochastic differential equations, Markov jump processes and stochastically interacting agents, and noise models including normal, Laplace and Poisson noise. We conclude that the proposed algorithm could improve the accuracy of parameter estimates for a broad spectrum of applications.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Monte-carlo; Statistical-inference; Model Selection; Simulation; Size
ISSN (print) / ISBN 1367-4803
Zeitschrift Bioinformatics
Quellenangaben Band: 36, Heft: 1 Seiten: 551-559, Artikelnummer: , Supplement: 1
Verlag Oxford University Press
Verlagsort Oxford
Begutachtungsstatus Peer reviewed
Förderungen German Federal Ministry of Education and Research
German Research Foundation