Invariant Theory for Maximum Likelihood Estimation

Explore the connections between maximum likelihood estimation and invariant theory in this 38-minute lecture by Anna Seigal from the University of Oxford. Delve into log-linear models and Gaussian group models as part of the Workshop on Real Algebraic Geometry and Algorithms for Geometric Constraint Systems. Learn how optimization problems in statistical models relate to stability concepts in invariant theory. Discover the research findings from Seigal's collaborative work with Carlos Améndola, Kathlén Kohn, and Philipp Reichenbach. Follow the lecture's progression through topics such as groups and statistics, MLE correspondence, and Gaussian group models, culminating in a comprehensive proof.

Introduction
Groups and Statistics
Maximum Likelihood Estimation
MLE
Correspondence
Gaussian Group Models
Proof