Maximum information divergence from linear and toric models
Statistics Theory
2023-09-07 v1 Information Theory
Algebraic Geometry
math.IT
Optimization and Control
Statistics Theory
Abstract
We study the problem of maximizing information divergence from a new perspective using logarithmic Voronoi polytopes. We show that for linear models, the maximum is always achieved at the boundary of the probability simplex. For toric models, we present an algorithm that combines the combinatorics of the chamber complex with numerical algebraic geometry. We pay special attention to reducible models and models of maximum likelihood degree one.
Cite
@article{arxiv.2308.15598,
title = {Maximum information divergence from linear and toric models},
author = {Yulia Alexandr and Serkan Hoşten},
journal= {arXiv preprint arXiv:2308.15598},
year = {2023}
}
Comments
33 pages, 6 figures