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Bayes factor

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The Bayes factor is a ratio of two competing statistical models represented by their evidence, and is used to quantify the support for one model over the other. The models in question can have a common set of parameters, such as a null hypothesis and an alternative, but this is not necessary; for instance, it could also be a non-linear model compared to its linear approximation. The Bayes factor can be thought of as a Bayesian analog to the likelihood-ratio test, although it uses the integrated (i.e., marginal) likelihood rather than the maximized likelihood. As such, both quantities only coincide under simple hypotheses (e.g., two specific parameter values). Also, in contrast with null hypothesis significance testing, Bayes factors support evaluation of evidence in favor of a null hypothesis, rather than only allowing the null to be rejected or not rejected.

Bayes factor - Wikipedia Jump to content From Wikipedia, the free encyclopedia Ratio of competing statistical models Part of a series on Bayesian statistics Posterior = Likelihood × Prior ÷ Evidence Background Bayesian inference Bayesian probability Bayes' theorem Bernstein–von Mises theorem Coherence Cox's theorem Cromwell's rule Likelihood principle Principle of indifference Principle of maximum entropy Model building Conjugate prior Linear regression Empirical Bayes Hierarchical model Posterior approximation Markov chain Monte Carlo Laplace's approximation Integrated nested Laplace approxim

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