The Bayes factor is a likelihood ratio of the marginal likelihood of two competing hypotheses, usually a null and an alternative. The posterior probability of a model M given data D is given by Bayes' theorem: The key data-dependent term 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 we have to choose between two models on the basis of observed data D, the plausibility of the two different models M1 and M2, parametrised by model parameter vectors and, is assessed by the Bayes factorK given by When the two models are equally probablea priori, so that, the Bayes factor is equal tothe 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. However, 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:
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 preferred status, and only considers evidence against it. Harold Jeffreys gave a scale for interpretation of K:
K
dHart
bits
Strength of evidence
The second column gives the corresponding weights of evidence in decihartleys ; bits are added in the third column for clarity. According to I. J. Good a change in a weight of evidence of 1 deciban or 1/3 of a bit is about as finely as humans can reasonably perceive their degree of belief in a hypothesis in everyday use. An alternative table, widely cited, is provided by Kass and Raftery :
log10K
K
Strength of evidence
Example
Suppose we have a random variable that produces either a success or a failure. We want to compare a model M1 where the probability of success is q = ½, and another model M2 where q is unknown and we take a prior distribution for q that is uniform on . We take a sample of 200, and find 115 successes and 85 failures. The likelihood can be calculated according to the binomial distribution: Thus we have for M1 whereas for M2 we have The ratio is then 1.197..., which is "barely worth mentioning" even if it points very slightly towards M1. A frequentisthypothesis test of M1 would have produced a very different result. Such a test says that M1 should be rejected at the 5% significance level, since the probability of getting 115 or more successes from a sample of 200 if q = ½ is 0.0200, and as a two-tailed test of getting a figure as extreme as or more extreme than 115 is 0.0400. 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 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, namely 115⁄200 = 0.575, whence . That gives a likelihood ratio of 0.1045 and points towards M2. M2 is a more complex model than M1 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 AIC value is 2·0 − 2·ln = 10.2467. Model M2 has 1 parameter, and so its AIC value is 2·1 − 2·ln = 7.7297. Hence M1 is about exp = 0.284 times as probable as M2 to minimize the information loss. Thus M2 is slightly preferred, but M1 cannot be excluded.
Application
Bayes factor has been applied to rank dynamic differential expression of genes instead of q-value.
