Eigenvalues and Eigenvectors
For those vectors, Px1 D x1 (steady state) and Px2 D 0 (nullspace). This example illustrates Markov matrices and singular matrices and (most important) symmetric matrices. All have special ’s and x’s: 1. Each column of P D:5 :5:5 :5 adds to 1,so D 1 is an eigenvalue. 2. P is singular,so D 0 is an eigenvalue. 3.
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