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 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.

• Publication year

  2018

• Venue

  PLoS ONE

• Publication date

  2018-03-08

• Fields of study

  Mathematics, Computer Science, Medicine

• Identifiers
  DOI 10.1371/journal.pone.0193981PMID 29518130PMCID 5843277
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Baysefactor: Computation of Bayes Factors for Common Designs

  2018cited by this paper
• UvA-DARE ( Digital Academic Repository ) Bayesian inference for psychology . Part

  2018cited by this paper
• A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models Using Warp-III Bridge Sampling

  2018cited by this paper
• The need for Bayesian hypothesis testing in psychological science

  2017cited by this paper
• The ASA Statement on p-Values: Context, Process, and Purpose

  2016cited by this paper
• Model Comparison and the Principle of Parsimony

  2015cited by this paper
• Reliable ABC model choice via random forests

  2014cited by this paper
• DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data

  2014cited by this paper
• The NumPy Array: A Structure for Efficient Numerical Computation

  2011cited by this paper
• abc: an R package for approximate Bayesian computation (ABC)

  2011cited by this paper
• Bayesian Versus Orthodox Statistics: Which Side Are You On?

  2011cited by this paper
• Likelihood-free estimation of model evidence

  2011cited by this paper
• Approximate Bayesian Computation (ABC) in practice.

  2010cited by this paper
• ABC-SysBio—approximate Bayesian computation in Python with GPU support

  2010cited by this paper
• Multinomial process tree models of control and automaticity in weapon misidentification

  2009influential reference
• Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

  2009cited by this paper
• Bayesian t tests for accepting and rejecting the null hypothesis

  2009cited by this paper
• Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

  2009cited by this paper
• Adaptive approximate Bayesian computation

  2008cited by this paper
• ABC likelihood-free methods for model choice in Gibbs random fields

  2008cited by this paper
• A practical solution to the pervasive problems ofp values

  2007cited by this paper
• Separating multiple processes in implicit social cognition: the quad model of implicit task performance.

  2005cited by this paper
• Stereotypes as dominant responses: on the "social facilitation" of prejudice in anticipated public contexts.

  2003cited by this paper
• Approximate Bayesian computation in population genetics.

  2002cited by this paper
• Random Forests

  2001cited by this paper
• Multinomial Modeling and the Measurement of Cognitive Processes

  2001cited by this paper
• Prejudice and perception: the role of automatic and controlled processes in misperceiving a weapon.

  2001cited by this paper
• Null hypothesis significance testing: a review of an old and continuing controversy.

  2000cited by this paper
• Stroop process dissociations: the relationship between facilitation and interference.

  1994cited by this paper
• A process dissociation framework: Separating automatic from intentional uses of memory

  1991cited by this paper
• Chapter 3: The Likelihood Principle and Generalizations

  1988cited by this paper
• Multinomial Modeling and the Measurement of Cognitive Processes.

  1988cited by this paper
• Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician

  1984cited by this paper

• Evaluation of Tolerance Selection Strategies and Multifidelity Techniques in ABC Methods

  2024cites this paper
• Extrapolating Metal (Cu, Ni, Zn) Toxicity from Individuals to Populations Across Daphnia Species Using Mechanistic Models: The Roles of Uncertainty Propagation and Combined Physiological Modes of Action

  2023cites this paper
• A Deep Learning Method for Comparing Bayesian Hierarchical Models

  2023cites this paper
• Model Comparison in Approximate Bayesian Computation

  2022cites this paper
• Julia Language in Machine Learning: Algorithms, Applications, and Open Issues

  2020cites this paper
• Towards end-to-end likelihood-free inference with convolutional neural networks.

  2020cites this paper
• BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks

  2020cites this paper
• Amortized Bayesian Model Comparison With Evidential Deep Learning

  2020cites this paper
• Sequential sampling models with variable boundaries and non-normal noise: A comparison of six models

  2019cites this paper
• SimPrily: A Python framework to simplify high-throughput genomic simulations

  2018cites this paper
• An Approximate Likelihood Perspective on ABC Methods

  2017cites this paper