But it can also be problematic since it can induce inconsistencies in, e.g., Hessian-based flatness measures,

We focus on a stochastic learning model where the learner observes a finite set of training examples and the output of the learning process is a data-dependent distribution over a space of hypotheses.

We develop scalable methods for producing conformal Bayesian predictive intervals with finite sample calibration guarantees.