Bayesian learning versus optimal learning

We consider the optimal performance that may be reached in the problem of learning the symmetry-breaking direction of a cloud of points in a -dimensional space. The performance is measured through the overlap between the true symmetry-breaking direction and the learnt one. Depending on the problem, the learning curves may present discontinuities. We show that close to these, bayesian learning is not optimal.