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Klinger, E. ; Rickert, D. ; Hasenauer, J.

pyABC: Distributed, likelihood-free inference.

Bioinformatics 34, 3591-3593 (2018)
Verlagsversion Postprint Forschungsdaten DOI PMC
Open Access Green
Likelihood-free methods are often required for inference in systems biology. While approximate Bayesian computation (ABC) provides a theoretical solution, its practical application has often been challenging due to its high computational demands. To scale likelihood-free inference to computationally demanding stochastic models, we developed pyABC: a distributed and scalable ABC-Sequential Monte Carlo (ABC-SMC) framework. It implements a scalable, runtime-minimizing parallelization strategy for multi-core and distributed environments scaling to thousands of cores. The framework is accessible to non-expert users and also enables advanced users to experiment with and to custom implement many options of ABC-SMC schemes, such as acceptance threshold schedules, transition kernels and distance functions without alteration of pyABC's source code. pyABC includes a web interface to visualize ongoing and finished ABC-SMC runs and exposes an API for data querying and post-processing.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Approximate Bayesian Computation; Sequential Monte-carlo; Parameter-estimation; Dynamical-systems
ISSN (print) / ISBN 1367-4803
e-ISSN 1367-4811
Zeitschrift Bioinformatics
Quellenangaben Band: 34, Heft: 20, Seiten: 3591-3593 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)