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Entropy 2019, 21(3), 272; https://doi.org/10.3390/e21030272

Maximum-Entropy Priors with Derived Parameters in a Specified Distribution

1,2,3,*
and
4,5
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
*
Author to whom correspondence should be addressed.
Received: 1 February 2019 / Revised: 7 March 2019 / Accepted: 8 March 2019 / Published: 12 March 2019
(This article belongs to the Section Astrophysics, Cosmology, and Black Holes)
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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. View Full-Text
Keywords: maximum entropy; Bayesian inference; prior; derived distribution; neutrino hierarchy maximum entropy; Bayesian inference; prior; derived distribution; neutrino hierarchy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Handley, W.; Millea, M. Maximum-Entropy Priors with Derived Parameters in a Specified Distribution. Entropy 2019, 21, 272.

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