  We show that MaxEnt is so rich that it can distinguish between any two
different mappings: there always exists a nonnegative weight vector which
assigns them different MaxEnt probabilities. Stochastic HG instead does admit
equiprobable mappings and we give a complete formal characterization of them.
We compare these different predictions of the two frameworks on a test case of
Finnish stress.
