A Comparison of Bayes Factor Approximation Methods Including Two New Methods

Bayes Factors play an important role in comparing the fit of models ranging from multiple regression to mixture models. Full Bayesian analysis calculates a Bayes Factor from an explicit prior distribution. However, computational limitations or lack of an appropriate prior sometimes prevent researchers from using an exact Bayes Factor. Instead, it is approximated, often using Schwarz’s (1978) Bayesian Information Criterion (BIC), or a variant of the BIC. In this paper we provide a comparison of several Bayes Factor approximations, including two new approximations, the SPBIC and IBIC. The SPBIC is justified by using a scaled unit information prior distribution that is more general than the BIC’s unit information prior, and the IBIC approximation utilizes more terms of approximation than in the BIC. In a simulation study we show that several measures perform well in large samples, that performance declines in smaller samples, and that SPBIC and IBIC can provide improvement to existing measures under some conditions, including small sample sizes. We then illustrate the use of the fit measures in an empirical example from the crime data of Ehrlich (1973). We conclude with recommendations for researchers.