PuSH - Publication Server of Helmholtz Zentrum München

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

pyABC: Distributed, likelihood-free inference.

Bioinformatics 34, 3591-3593 (2018)
Publ. Version/Full Text Postprint Research data 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.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Approximate Bayesian Computation; Sequential Monte-carlo; Parameter-estimation; Dynamical-systems
ISSN (print) / ISBN 1367-4803
e-ISSN 1367-4811
Journal Bioinformatics
Quellenangaben Volume: 34, Issue: 20, Pages: 3591-3593 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)