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.

翻译：我们研究了代数统计中线性浓度模型的最大可能性。我们把对等种类的几何与半无限期编程的几何联系起来。我们表明,格拉斯马尼亚的Zariski关闭没有达到最大可能的最大可能性的线性空间与Zariski关闭这组线性空间相吻合,这组线性空间用非封闭的半无限期阳性锥体图像界定了预测。特别是,这显示这种关闭是共生地表的结合。