<p>A proposition is <i>derivationally robust</i> precisely when it is a prediction of each model in an ensemble of models. We show that a recent and influential Bayesian defense of the epistemic merits of derivational robustness analysis faces substantial obstacles. The main reason for this is that a Bayesian characterization of derivational robustness requires one to condition on a logical or mathematical truth. Standardly, however, conditioning on a logical or mathematical truth cannot raise or lower the probability of any proposition, which means confirmation in such cases is impossible. What is required for a Bayesian defense of derivational robustness to be plausible is the adoption of a non-standard probability calculus, but the details of such an account have yet to be worked out in sufficient detail. We close by arguing against the view that agreement among multiple models has a confirmatory status that is the same or similar to agreement among multiple measurements of some quantity. It is straightforward to show that agreement among measurements has epistemic significance. This is not true for derivational robustness.</p>