ABrox—A user-friendly Python module for approximate Bayesian computation with a focus on model comparison

• Published in: PloS one Vol. 13; no. 3; p. e0193981
• Main Authors: Mertens, Ulf Kai, Voss, Andreas, Radev, Stefan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.03.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Approximation; Bayesian analysis; Bioinformatics; Computation; Computer and Information Sciences; Economic models; Engineering and Technology; Freeware; Graphical user interface; Hypotheses; Mathematical models; Normal distribution; Open source software; Parameter estimation; Personality; Physical Sciences; Research and Analysis Methods; Social psychology; Software packages; Source code; Statistical analysis; Statistical tests; Stochastic methods; Stochastic processes; Stochasticity; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0193981

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.