Toric invariant theory for maximum likelihood estimation in log-linear models

DOI10.2140/astat.2021.12.187zbMath1487.14100arXiv2012.07793OpenAlexW3111638709MaRDI QIDQ2076293

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

Publication date: 16 February 2022

Published in: Algebraic Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.07793

zbMATH Keywords

maximum likelihood estimationgraphical modelstorus actionslog-linear modelsnull conescaling algorithms

Mathematics Subject Classification ID

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

Related Items (1)

Toric geometry of entropic regularization

Cites Work

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• Maximum likelihood estimation in log-linear models
• Log-linear models for frequency tables derived by indirect observation: Maximum likelihood equations
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• Biology
• Invariant Theory and Scaling Algorithms for Maximum Likelihood Estimation
• A Relationship Between Arbitrary Positive Matrices and Doubly Stochastic Matrices
• An Iterative Procedure for Estimation in Contingency Tables
• Generalized Iterative Scaling for Log-Linear Models
• On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known
• Orbits of linear algebraic groups

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