Title
The maximum likelihood degree of toric varieties.
Abstract
We study the maximum likelihood (ML) degree of toric varieties, known as discrete exponential models in statistics. By introducing scaling coefficients to the monomial parameterization of the toric variety, one can change the ML degree. We show that the ML degree is equal to the degree of the toric variety for generic scalings, while it drops if and only if the scaling vector is in the locus of the principal A-determinant. We also illustrate how to compute the ML estimate of a toric variety numerically via homotopy continuation from a scaled toric variety with low ML degree. Throughout, we include examples motivated by algebraic geometry and statistics. We compute the ML degree of rational normal scrolls and a large class of Veronese-type varieties. In addition, we investigate the ML degree of scaled Segre varieties, hierarchical log-linear models, and graphical models.
Year
DOI
Venue
2019
10.1016/j.jsc.2018.04.016
Journal of Symbolic Computation
Keywords
Field
DocType
Maximum likelihood degree,Toric variety,A-discriminant
Toric variety,Discrete mathematics,Algebraic geometry,Combinatorics,Parametrization,Maximum likelihood,Exponential models,Graphical model,Monomial,Scaling,Mathematics
Journal
Volume
ISSN
Citations 
92
0747-7171
0
PageRank 
References 
Authors
0.34
4
9