24.04.2017 um 16:15 Uhr in 69/125:
Seth Sullivant (North Carolina State University)
Algebraic Geometry of Gaussian Graphical Models
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. This is joint work with Alex Fink and Jenna Rajchgot.