Friday, 15:15 - 15:40 h, Room: H 2036

 

Caroline Uhler
Maximum likelihood estimation in Gaussian graphical models from the perspective of convex algebraic geometry

 

Abstract:
We study multivariate normal models that are described by linear
constraints on the inverse of the covariance matrix. Maximum
likelihood estimation for such models leads to the problem of maximizing the determinant function over a spectrahedron, and to the problem of characterizing the image of the positive definite cone under an arbitrary linear projection. We examine these problems at the interface of statistics and conic optimization from the perspective of convex algebraic geometry.

 

Talk 1 of the invited session Fri.3.H 2036
"Algebraic geometry and conic programming III" [...]
Cluster 4
"Conic programming" [...]

 

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