Meta-Uncertainty in Bayesian Model Comparison

Understand

Bayesian model comparison (BMC) offers a principled probabilistic approach to study and rank competing models.

• In standard BMC, we construct a discrete probability distribution over the set of possible models, conditional on the observed data of interest.
• These posterior model probabilities (PMPs) are measures of uncertainty, but -- when derived from a finite number of observations -- are also uncertain themselves.
• In this paper, we conceptualize distinct levels of uncertainty which arise in BMC.