Damned by Faint Praise is the phenomenon whereby weak positive information leads to a negative change in belief. However, in a Bayesian model of belief revision positive information can seemingly only exert a positive change in belief. We introduce a version of Bayes Theorem incorporating the concept of epistemic closure. This reformalization is able to predict the conditions under which a damned by faint praise effect is observed. Moreover, good, parameter-free fits are observed between the Bayesian model and the experimental data. This provides further support for the Bayesian approach to informal argumentation (e.g., Hahn & Oaksford, 2007).