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Verfasst von:Mertens, Ulf K. [VerfasserIn]   i
 Voß, Andreas [VerfasserIn]   i
 Radev, Stefan [VerfasserIn]   i
Titel:ABrox
Titelzusatz:a user-friendly Python module for approximate Bayesian computation with a focus on model comparison
Verf.angabe:Ulf Kai Mertens, Andreas Voss, Stefan Radev
Fussnoten:Gesehen am 29.03.2018
Titel Quelle:Enthalten in: PLOS ONE
Jahr Quelle:2018
Band/Heft Quelle:13(2018,3) Artikel-Nummer e0193981, 16 Seiten
ISSN Quelle:1932-6203
Abstract:We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation. Our new open-source software called ABrox is used to illustrate ABC for model comparison on two prominent statistical tests, the two-sample t-test and the Levene-Test. We further highlight the flexibility of ABC compared to classical Bayesian hypothesis testing by computing an approximate Bayes factor for two multinomial processing tree models. Last but not least, throughout the paper, we introduce ABrox using the accompanied graphical user interface.
DOI:doi:10.1371/journal.pone.0193981
URL:Kostenfrei: Verlag: http://dx.doi.org/10.1371/journal.pone.0193981
 Kostenfrei: Verlag: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193981
 DOI: https://doi.org/10.1371/journal.pone.0193981
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1571535144
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