Maximum-Entropy Priors with Derived Parameters in a Specified Distribution

Understand

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.