Maximizing Multi-Information
Nihat Ay, Andreas Knauf
Abstract
Stochastic interdependence of a probablility distribution on a product space is measured by its Kullback-Leibler distance from the exponential family of product distributions (called multi-information). Here we investigate low-dimensional exponential families that contain the maximizers of stochastic interdependence in their closure. Based on a detailed description of the structure of probablility distributions with globally maximal multi-information we obtain our main result: The exponential family of pure pair-interactions contains all global maximizers of the multi-information in its closure.
Create a lesson
Related papers
Phase transitions in non-Hermitian spherical integrals
Pierre Bousseyroux, Marc Potters
Factorization method for a clamped obstacle from near-field measurements via a far-field transformation
General Ozochiawaeze, Isaac Harris
Asymmetric phase transitions in random noncommutative geometries
Benedek Bukor, Masoud Khalkhali, Samuel Kováčik et al.
A Cumulative Framework for Solid Deformation
Lev Steinberg
Classification of pairs of second-order Hamiltonian operators and hydrodynamic type systems in six components
Giorgio Gubbiotti, Lambertus Van Geemen, Pierandrea Vergallo
Reconstructability of Inverse Problems under Symmetry: Separating Structural, Effective, and Physical Upper Bounds
Isshin Arai