  In this paper we propose an identification procedure of a sparse graphical
model associated to a Gaussian stationary stochastic process. The
identification paradigm exploits the approximation of autoregressive processes
through reciprocal processes in order to improve the robustness of the
identification algorithm, especially when the order of the autoregressive
process becomes large. We show that the proposed paradigm leads to a
regularized, circulant matrix completion problem whose solution only requires
computations of the eigenvalues of matrices of dimension equal to the dimension
of the process.
