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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Vydané v:PloS one Ročník 13; číslo 3; s. e0193981
Hlavní autori: Mertens, Ulf Kai, Voss, Andreas, Radev, Stefan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 08.03.2018
Public Library of Science (PLoS)
Predmet:
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
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0193981