Maximum-Entropy Priors with Derived Parameters in a Specified Distribution

Will Handley; Marius Millea

doi:10.3390/e21030272

Maximum-Entropy Priors with Derived Parameters in a Specified Distribution

Entropy (Basel). 2019 Mar 12;21(3):272. doi: 10.3390/e21030272.

Authors

Will Handley^{1

2

3}, Marius Millea^{4

5}

Affiliations

¹ Astrophysics Group, Cavendish Laboratory, J.J.Thomson Avenue, Cambridge CB3 0HE, UK.
² Kavli Institute for Cosmology, Madingley Road, Cambridge CB3 0HA, UK.
³ Gonville & Caius College, Trinity Street, Cambridge CB2 1TA, UK.
⁴ Institut dAstrophysique de Paris (IAP), UMR 7095, CNRS UPMC Universit Paris 6, Sorbonne Universits, 98bis Boulevard Arago, F-75014 Paris, France.
⁵ Institut Lagrange de Paris (ILP), Sorbonne Universits, 98bis Boulevard Arago, F-75014 Paris, France.

Abstract

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.

Keywords: Bayesian inference; derived distribution; maximum entropy; neutrino hierarchy; prior.