Conference abstracts

Session A2 - Computational Algebraic Geometry

July 10, 17:00 ~ 17:25

Algebraic Geometry of Gaussian Graphical Models

Seth Sullivant

North Carolina State University, USA   -

Gaussian graphical models are statistical models widely used for modeling complex interactions between collections of linearly related random variables. A graph is used to encode recursive linear relationships with correlated error terms. These models a subalgebraic subsets of the cone of positive definite matrices, that generalize familiar objects in combinatorial algebraic geometry like toric varieties and determinantal varieties. I will explain how the study of the equations of these models is related to matrix Schubert varieties.

Joint work with Alex Fink (Queen Mary University -- London) and Jenna Rajchgot (University of Saskatchewan).

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FoCM 2017, based on a nodethirtythree design.