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A matrix is positive semidefinite if: 1. or 2. All eigenvalues of are
Finite Variance = Autocorrelation symmetric + positive semidefinite
Positive Definite = Positive Eigenvalues
Positive Semidefinite has dim Eigenvectors
Convex
Controllability Gramian
Observability Gramian
Linear Quadratic Problem
Kalman Filter
Hadamard's Inequality
KL Transform Decorrelates X
Closed-loop Predictor Coefficients