Maximum-Entropy Priors with Derived Parameters in a Specified
  Distribution
  We propose a method for transforming probability distributions so that
parameters of interest are forced into a specified distribution. We prove that
this approach is the maximum entropy choice, and provide a motivating example
applicable to neutrino hierarchy inference.
