  In this paper we propose an identification method for latent-variable
graphical models associated to autoregressive (AR) Gaussian stationary
processes. The identification procedure exploits the approximation of AR
processes through stationary reciprocal processes thus benefiting of the
numerical advantages of dealing with block-circulant matrices. These advantages
become more and more significant as the order of the process gets large. We
show how the identification can be cast in a regularized convex program and we
present numerical examples that compares the performances of the proposed
method with the existing ones.
