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Interpreting the Metropolis Algorithm

The Metropolis method is simply the power method for computing the right eigenvector of \boldsymbol{M} with the largest magnitude eigenvalue. By construction, the correct probability distribution is a right eigenvector with eigenvalue 1. Therefore, for the Metropolis method to converge to this result, we must show that \boldsymbol{M} has only one eigenvalue with this magnitude, and all other eigenvalues are smaller.

Even a defective matrix has at least one left and right eigenvector for each eigenvalue. An example of a defective matrix is

\begin{bmatrix} 0 & 1\\ 0 & 0 \\ \end{bmatrix},

with two zero eigenvalues, only one right eigenvector

\begin{bmatrix} 1 \\ 0\\ \end{bmatrix}

and only one left eigenvector (0\ 1) .