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Abstract: We study multivariate normal models that are described by linear constraintson the inverse of the covariance matrix. Maximum likelihood estimation for suchmodels leads to the problem of maximizing the determinant function over aspectrahedron, and to the problem of characterizing the image of the positivedefinite cone under an arbitrary linear projection. These problems at theinterface of statistics and optimization are here examined from the perspectiveof convex algebraic geometry.



Autor: Bernd Sturmfels, Caroline Uhler

Fuente: https://arxiv.org/



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