学术文献库

Bayesian model comparison is often based on the posterior distribution over the set of compared models. This distribution is often observed to concentrate on a single model even when other measures of model fit or forecasting ability indicate no strong preference. Furthermore, a moderate change in the data sample can easily shift the posterior model probabilities to concentrate on another model. We document overconfidence in two high-profile applications in economics and neuroscience. To shed more light on the sources of overconfidence we derive the sampling variance of the Bayes factor in univariate and multivariate linear regression. The results show that overconfidence is likely to happen when i) the compared models give very different approximations of the data-generating process, ii) the models are very flexible with large degrees of freedom that are not shared between the models, and iii) the models underestimate the true variability in the data.