A PAC analysis of a Bayesian estimator

Bayesian analysis of generalisation can place a prior distribution on the hypotheses and estimate the volume of this space that is consistent with the training data. The larger this volume the greater the confidence in the classifier obtained. The key feature of such estimators is that they provide a posteriori estimates of generalisation based on properties of the hypothesis and the training data. This contrasts with a ‘classical’PAC analysis which provides only a priori (worst case) bounds.