  The paper proposes an identification procedure for autoregressive gaussian
stationary stochastic processes wherein the manifest (or observed) variables
are mostly related through a limited number of latent (or hidden) variables.
The method exploits the sparse plus low-rank decomposition of the inverse of
the manifest spectral density and the efficient convex relaxations recently
proposed for such decomposition.
