<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Bayesian inference affords scientists powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support for one hypothesis over another is updated after seeing the data. Part of the hesitance to adopt this approach may stem from an unfamiliarity with the computational tools necessary for computing Bayes factors. Previous work has shown that closed-form approximations of Bayes factors are relatively easy to obtain for between-groups methods, such as an analysis of variance or t-test. In this paper, I extend this approximation to develop a formula for the Bayes factor that directly uses information that is typically reported for ANOVAs (e.g., the F ratio and degrees of freedom). After giving two examples of its use, I report the results of simulations which show that even with minimal input, this approximate Bayes factor produces similar results to existing software solutions.</p></abstract>

Bayesian inference affords scientists powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support for one hypothesis over another is updated after seeing the data. Part of the hesitance to adopt this approach may stem from an unfamiliarity with the computational tools necessary for computing Bayes factors. Previous work has shown that closed-form approximations of Bayes factors are relatively easy to obtain for between-groups methods, such as an analysis of variance or t-test. In this paper, I extend this approximation to develop a formula for the Bayes factor that directly uses information that is typically reported for ANOVAs (e.g., the F ratio and degrees of freedom). After giving two examples of its use, I report the results of simulations which show that even with minimal input, this approximate Bayes factor produces similar results to existing software solutions.

Computing Bayes factors to measure evidence from experiments: An extension of the BIC approximation

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Bayesian inference affords scientists powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support...

Computing Bayes factors to measure evidence from experiments: An extension of the BIC approximation

Publié en ligne: 22 juin 2018

Pages: 31 - 43

DOI: https://doi.org/10.2478/bile-2018-0003

Mots clés
Bayes factors, Bayesian inference, analysis of variance, hypothesis testing

© 2018 Thomas J. Faulkenberry, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Computing Bayes factors to measure evidence from experiments: An extension of the BIC approximation

Publié en ligne: 22 juin 2018

Pages: 31 - 43

DOI: https://doi.org/10.2478/bile-2018-0003

Mots clésBayes factors, Bayesian inference, analysis of variance, hypothesis testing

© 2018 Thomas J. Faulkenberry, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Bayes factors, Bayesian inference, analysis of variance, hypothesis testing