Jaynes-Gibbs Entropic Convex Duals and Orthogonal Polynomials

• Published in: Entropy (Basel, Switzerland) Switzerland), 2022-05, Vol.24 (5), p.709
• Main Author: Le Blanc, Richard
• Format: Article
• Language: English
• Subjects: Bayesian inference; Duality principle; entropic convex dual; Functions (mathematics); Geophysics; Gibbs prior; Hermite polynomials; Hyperspheres; Inverse problems; Jaynes’ maximal entropy principle; noncentral distributions; Normal distribution; orthogonal polynomials; Polynomials; Random variables; Thermal expansion; Translations
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24050709

The univariate noncentral distributions can be derived by multiplying their central distributions with translation factors. When constructed in terms of translated uniform distributions on unit radius hyperspheres, these translation factors become generating functions for classical families of orthogonal polynomials. The ultraspherical noncentral , normal , , and χ2 distributions are thus found to be associated with the Gegenbauer, Hermite, Jacobi, and Laguerre polynomial families, respectively, with the corresponding central distributions standing for the polynomial family-defining weights. Obtained through an unconstrained minimization of the Gibbs potential, Jaynes' maximal entropy priors are formally expressed in terms of the empirical densities' entropic convex duals. Expanding these duals on orthogonal polynomial bases allows for the expedient determination of the Jaynes-Gibbs priors. Invoking the moment problem and the duality principle, modelization can be reduced to the direct determination of the prior moments in parametric space in terms of the Bayes factor's orthogonal polynomial expansion coefficients in random variable space. Genomics and geophysics examples are provided.