Differential Equations for Gaussian Statistical Models with Rational Maximum Likelihood Estimator

Fuente: arXiv
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Main Authors: Améndola, Carlos, Gustafsson, Lukas, Kohn, Kathlén, Marigliano, Orlando, Seigal, Anna
Format: Preprint
Published: 2023
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author Améndola, Carlos
Gustafsson, Lukas
Kohn, Kathlén
Marigliano, Orlando
Seigal, Anna
author_facet Améndola, Carlos
Gustafsson, Lukas
Kohn, Kathlén
Marigliano, Orlando
Seigal, Anna
contents We study multivariate Gaussian statistical models whose maximum likelihood estimator (MLE) is a rational function of the observed data. We establish a one-to-one correspondence between such models and the solutions to a nonlinear first-order partial differential equation (PDE). Using our correspondence, we reinterpret familiar classes of models with rational MLE, such as directed (and decomposable undirected) Gaussian graphical models. We also find new models with rational MLE. For linear concentration models with rational MLE, we show that homaloidal polynomials from birational geometry lead to solutions to the PDE. We thus shed light on the problem of classifying Gaussian models with rational MLE by relating it to the open problem in birational geometry of classifying homaloidal polynomials.
format Preprint
id arxiv_https___arxiv_org_abs_2304_12054
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Differential Equations for Gaussian Statistical Models with Rational Maximum Likelihood Estimator
Améndola, Carlos
Gustafsson, Lukas
Kohn, Kathlén
Marigliano, Orlando
Seigal, Anna
Algebraic Geometry
Statistics Theory
62R01, 62F10, 62H22, 14E05, 35C11, 35F20
We study multivariate Gaussian statistical models whose maximum likelihood estimator (MLE) is a rational function of the observed data. We establish a one-to-one correspondence between such models and the solutions to a nonlinear first-order partial differential equation (PDE). Using our correspondence, we reinterpret familiar classes of models with rational MLE, such as directed (and decomposable undirected) Gaussian graphical models. We also find new models with rational MLE. For linear concentration models with rational MLE, we show that homaloidal polynomials from birational geometry lead to solutions to the PDE. We thus shed light on the problem of classifying Gaussian models with rational MLE by relating it to the open problem in birational geometry of classifying homaloidal polynomials.
title Differential Equations for Gaussian Statistical Models with Rational Maximum Likelihood Estimator
topic Algebraic Geometry
Statistics Theory
62R01, 62F10, 62H22, 14E05, 35C11, 35F20
url https://arxiv.org/abs/2304.12054