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Homaloidal polynomials and Gaussian models of maximum likelihood degree 1

Shelby Cox, Pratik Misra and Pardis Semnani

Vol. 15 (2024), No. 2, 167–198
Abstract

We study the Gaussian statistical models whose log-likelihood function has a unique complex critical point, i.e., has maximum likelihood degree 1. We exploit the connection developed by Améndola et al. between the models having maximum likelihood degree 1 and homaloidal polynomials. We study the spanning tree generating function of a graph and show this polynomial is homaloidal when the graph is chordal. When the graph is a cycle on n vertices, n 4, we prove the polynomial is not homaloidal, and show that the maximum likelihood degree of the resulting model is the (n1)-th Eulerian number. These results support our conjecture that the spanning tree generating function is a homaloidal polynomial if and only if the graph is chordal. We also provide an algebraic formulation for the defining equations of these models. Using existing results, we provide a computational study on constructing new families of homaloidal polynomials. In the end, we analyze the symmetric determinantal representation of such polynomials and provide an upper bound on the size of the matrices involved.

Keywords
homaloidal polynomial, maximum likelihood degree, multivariate normal distribution, Gaussian graphical model, symmetric determinantal representation
Mathematical Subject Classification
Primary: 14E05, 62H22, 62R01
Secondary: 14M99
Milestones
Received: 8 February 2024
Revised: 24 September 2024
Accepted: 12 October 2024
Published: 3 December 2024
Authors
Shelby Cox
University of Michigan
Ann Arbor, MI
United States
Pratik Misra
Technical University of Munich
Munich
Germany
Pardis Semnani
University of British Columbia
Vancouver, BC
Canada