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.

Although conceptually simple, the computation of the Bayes factor can be challenging depending on the complexity of the model and the hypotheses. Since closed-form expressions of the marginal likelihood are generally not available, numerical approximations based on MCMC samples have been suggested. For certain special cases, simplified algebraic expressions can be derived; for instance, the Savage–Dickey density ratio in the case of a precise (equality constrained) hypothesis against an unrestricted alternative. Another approximation, derived by applying Laplace's approximation to the integrated likelihoods, is known as the Bayesian information criterion (BIC); in large data sets the Bayes factor will approach the BIC as the influence of the priors wanes. In small data sets, priors generally matter and must not be improper since the Bayes factor will be undefined if either of the two integrals in its ratio is not finite.

Definition

The Bayes factor is the ratio of two marginal likelihoods; that is, the likelihoods of two statistical models integrated over the prior probabilities of their parameters.

The posterior probability of a model M given data D is given by Bayes' theorem:

represents the probability that some data are produced under the assumption of the model M; evaluating it correctly is the key to Bayesian model comparison.

Given a model selection problem in which one wishes to choose between two models on the basis of observed data D, the plausibility of the two different models M1 and M2, is assessed by the Bayes factor K given by

, the Bayes factor is equal to the ratio of the posterior probabilities of M1 and M2. If instead of the Bayes factor integral, the likelihood corresponding to the maximum likelihood estimate of the parameter for each statistical model is used, then the test becomes a classical likelihood-ratio test. Unlike a likelihood-ratio test, this Bayesian model comparison does not depend on any single set of parameters, as it integrates over all parameters in each model (with respect to the respective priors). An advantage of the use of Bayes factors is that it automatically, and quite naturally, includes a penalty for including too much model structure. It thus guards against overfitting. For models where an explicit version of the likelihood is not available or too costly to evaluate numerically, approximate Bayesian computation can be used for model selection in a Bayesian framework, with the caveat that approximate-Bayesian estimates of Bayes factors are often biased.

Other approaches are:

• to treat model comparison as a decision problem, computing the expected value or cost of each model choice;

• to use minimum message length (MML).

• to use minimum description length (MDL).

Interpretation

A value of K > 1 means that M1 is more strongly supported by the data under consideration than M2. Note that classical hypothesis testing gives one hypothesis (or model) preferred status (the 'null hypothesis'), and only considers evidence against it. The fact that a Bayes factor can produce evidence for and not just against a null hypothesis is one of the key advantages of this analysis method.

Harold Jeffreys gave a scale (Jeffreys' scale:

! K !! dHart !! bits !! Strength of evidence |- | < 100 || < 0 || < 0 || Negative (supports M2) |- | 100 to 101/2 || 0 to 5 || 0 to 1.6 || Barely worth mentioning |- | 101/2 to 101 || 5 to 10 || 1.6 to 3.3 || Substantial |- | 101 to 103/2 || 10 to 15 || 3.3 to 5.0 || Strong |- | 103/2 to 102 || 15 to 20 || 5.0 to 6.6 || Very strong |- | > 102 || > 20 || > 6.6 || Decisive |- |}

The second column gives the corresponding weights of evidence in decihartleyss (also known as decibans); bits.

An alternative table, widely cited, is provided by Kass and Raftery (1995):

! log10 K !! K !! Strength of evidence |- | 0 to 1/2 || 1 to 3.2 || Not worth more than a bare mention |- | 1/2 to 1 || 3.2 to 10 || Substantial |- | 1 to 2 || 10 to 100 || Strong |- | > 2 || > 100 || Decisive |- |} According to I. J. Good, the just-noticeable difference of humans in their everyday life, when it comes to a change degree of belief in a hypothesis, is about a factor of 1.3x, or 1 deciban, or 1/3 of a bit, or from 1:1 to 5:4 in odds ratio.

Example

Suppose we have a random variable where the probability of success is q = where q is unknown and we take a prior distribution for q that is uniform on [0,1]. We take a sample of 200, and find 115 successes and 85 failures. The likelihood can be calculated according to the binomial distribution:

we have

.

A frequentist hypothesis test (here considered as a null hypothesis should be rejected at the 5% significance level, since the probability of getting 115 or more successes from a sample of 200 if q = is 0.02, and as a two-tailed test of getting a figure as extreme as or more extreme than 115 is 0.04. Note that 115 is more than two standard deviations away from 100. Thus, whereas a frequentist hypothesis test would yield significant results at the 5% significance level, the Bayes factor hardly considers this to be an extreme result. Note, however, that a non-uniform prior (for example one that reflects that you expect the number of success and failures to be of the same order of magnitude) could result in a Bayes factor that is more in agreement with the frequentist hypothesis test.

A classical likelihood-ratio test would have found the maximum likelihood estimate for q, whence

(rather than averaging over all possible q). That gives a likelihood ratio of 0.1 and points towards M2.

because it has a free parameter which allows it to model the data more closely. The ability of Bayes factors to take this into account is a reason why Bayesian inference has been put forward as a theoretical justification for and generalisation of Occam's razor, reducing Type I errors.

On the other hand, the modern method of relative likelihood takes into account the number of free parameters in the models, unlike the classical likelihood ratio. The relative likelihood method could be applied as follows. Model M1 has 0 parameters, and so its Akaike information criterion. Model M2. Hence M1 times as probable as M2 to minimize the information loss. Thus M2 is slightly preferred, but M1 cannot be excluded.

See also

• Akaike information criterion

• Approximate Bayesian computation

• Bayesian information criterion

• Deviance information criterion

• Lindley's paradox

• Minimum message length

• Model selection

• E-Value

• Statistical ratios

• Odds ratio

• Relative risk

References

Further reading

• Dienes, Z. (2019). How do I know what my theory predicts? Advances in Methods and Practices in Psychological Science

• Jaynes, E. T. (1994), Probability Theory: the logic of science , chapter 24.

External links

• an R package for computing Bayes factors in common research designs

• Online calculator for informed Bayes factors

• web-based version of much of the BayesFactor package