One Size Does Not Fit All: proposal for a prior-adapted BIC

This paper presents a refinement of the Bayesian Information Criterion (BIC). While the original BIC selects models on the basis of com-plexity and fit, the so-called prior-adapted BIC allows us to choose among statistical models that differ on three scores: fit, complexity, and model size. The prior-adapted BIC can therefore accommodate comparisons among statistical models that differ only in the admissi-ble parameter space, e.g., for choosing among models with different constraints on the parameters. The paper ends with an application of this idea to a well-known puzzle from the psychology of reasoning, the conjunction fallacy. ∗Correspondence should be sent to the first author: Faculty of Philosophy, Universit