Linear spaces of symmetric matrices with non-maximal maximum likelihood degree

  • Y. Jiang Harvard University, Massachusetts, USA
  • K. Kohn KTH Royal Institute of Technology, Sweden
  • R. Winter MPI for Mathematics in the Sciences, Germany


We study the maximum likelihood degree of linear concentration models in algebraic statistics. We relate the geometry of the reciprocal variety to that of semidefinite programming. We show that the Zariski closure in the Grassmanian of the set of linear spaces that do not attain their maximal possible maximum likelihood degree coincides with the Zariski closure of the set of linear spaces defining a projection with non-closed image of the positive semidefinite cone. In particular, this shows that this closure is a union of coisotropic hypersurfaces.