Аннотация:
Conformal prediction is a method of producing prediction sets that can be applied on top of a wide
range of prediction algorithms. The method has a guaranteed coverage probability under the stan-
dard IID assumption regardless of whether the assumptions (often considerably more restrictive)
of the underlying algorithm are satisfied. However, for the method to be really useful it is desir-
able that in the case where the assumptions of the underlying algorithm are satisfied, the conformal
predictor loses little in efficiency as compared with the underlying algorithm (whereas being a con-
formal predictor, it has the stronger guarantee of validity). In this work we explore the degree to
which this additional requirement of efficiency is satisfied in the case of Bayesian ridge regression;
we find that asymptotically conformal prediction sets differ little from ridge regression prediction
intervals when the standard Bayesian assumptions are satisfied.