ABrox: a user-friendly Python module for approximate Bayesian computation with a focus on model comparison
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 pro...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
March 8, 2018
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| In: |
PLOS ONE
Year: 2018, Jahrgang: 13, Heft: 3 |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0193981 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1371/journal.pone.0193981 Verlag, kostenfrei, Volltext: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193981 
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| Verfasserangaben: | Ulf Kai Mertens, Andreas Voss, Stefan Radev |
| Zusammenfassung: | 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. |
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| Beschreibung: | Gesehen am 29.03.2018 |
| Beschreibung: | Online Resource |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0193981 |