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Schälte, Y. ; Hasenauer, J.

Informative and adaptive distances and summary statistics in sequential approximate Bayesian computation.

PLoS ONE 18:e0285836 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Calibrating model parameters on heterogeneous data can be challenging and inefficient. This holds especially for likelihood-free methods such as approximate Bayesian computation (ABC), which rely on the comparison of relevant features in simulated and observed data and are popular for otherwise intractable problems. To address this problem, methods have been developed to scale-normalize data, and to derive informative low-dimensional summary statistics using inverse regression models of parameters on data. However, while approaches only correcting for scale can be inefficient on partly uninformative data, the use of summary statistics can lead to information loss and relies on the accuracy of employed methods. In this work, we first show that the combination of adaptive scale normalization with regression-based summary statistics is advantageous on heterogeneous parameter scales. Second, we present an approach employing regression models not to transform data, but to inform sensitivity weights quantifying data informativeness. Third, we discuss problems for regression models under non-identifiability, and present a solution using target augmentation. We demonstrate improved accuracy and efficiency of the presented approach on various problems, in particular robustness and wide applicability of the sensitivity weights. Our findings demonstrate the potential of the adaptive approach. The developed algorithms have been made available in the open-source Python toolbox pyABC.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Monte-carlo; Systems
ISSN (print) / ISBN 1932-6203
Zeitschrift PLoS ONE
Quellenangaben Band: 18, Heft: 5, Seiten: , Artikelnummer: e0285836 Supplement: ,
Verlag Public Library of Science (PLoS)
Verlagsort Lawrence, Kan.
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Schlegel Professorship
Joachim Herz Foundation
German Research Foundation (DFG) under Germany's Excellence Strategy
German Federal Ministry of Education and Research (BMBF)