Invariant Theory and Scaling Algorithms for Maximum Likelihood Estimation

DOI10.1137/20M1328932MaRDI QIDQ5001672

Anna Leah Seigal, Kathlén Kohn, Carlos Améndola, Philipp Reichenbach

Publication date: 23 July 2021

Published in: SIAM Journal on Applied Algebra and Geometry (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.13662

zbMATH Keywords

maximum likelihood estimationgroup actionsgraphical modelsGaussian modelsnull conescaling algorithms

Mathematics Subject Classification ID

Point estimation (62F10) Geometric invariant theory (14L24) Real algebraic sets (14P05) Applications of linear algebraic groups to the sciences (20G45) Probabilistic graphical models (62H22) Algebraic statistics (62R01)

Related Items (12)

Maximum likelihood estimation for tensor normal models via castling transforms ⋮ The Maximum Likelihood Degree of Sparse Polynomial Systems ⋮ Principal components along quiver representations ⋮ A combinatorial algorithm for computing the entire sequence of the maximum degree of minors of a generic partitioned polynomial matrix with \(2 \times 2\) submatrices ⋮ Ranks of linear matrix pencils separate simultaneous similarity orbits ⋮ Nonlinear algebra and applications ⋮ Symmetries in directed Gaussian graphical models ⋮ Interactions of computational complexity theory and mathematics ⋮ Existence and uniqueness of the Kronecker covariance MLE ⋮ Toric invariant theory for maximum likelihood estimation in log-linear models ⋮ Maximum Likelihood Estimation for Matrix Normal Models via Quiver Representations ⋮ On Gaussian group convex models

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