Work

Maximum-Entropy Priors with Derived Parameters in a Specified Distribution

Year: 2019

Type: article

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.

Source: Entropy

DOI: 10.3390/e21030272

Authors: Will Handley, Marius Millea

Institutions: University of Cambridge, Centre National de la Recherche Scientifique, Institut d'Astrophysique de Paris, Sorbonne Université, Université Pierre-et-Marie-Curie, Institut Lagrange de Paris

Language: English

Topic: Statistical Mechanics and Entropy

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,621

Funder: Agence Nationale de la Recherche

Awards: G4820567559, Institute Lagrange of Paris, G7240651115, G7512206404

OpenAlex ID: W2799211024

FWCI: 1.498

Cites: 15

Cited by: 23

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