  We study learning of probability distributions characterized by an unknown
symmetry direction. Based on an entropic performance measure and the
variational method of statistical mechanics we develop exact upper and lower
bounds on the scaled critical number of examples below which learning of the
direction is impossible. The asymptotic tightness of the bounds suggests an
asymptotically optimal method for learning nonsmooth distributions.
