AR Identification of Latent-Variable Graphical Models

@article{Zorzi2016ARIO,
  title={AR Identification of Latent-Variable Graphical Models},
  author={Mattia Zorzi and Rodolphe Sepulchre},
  journal={IEEE Transactions on Automatic Control},
  year={2016},
  volume={61},
  pages={2327-2340}
}
The paper proposes an identification procedure for autoregressive Gaussian stationary stochastic processes under the assumption that the manifest (or observed) variables are nearly independent when conditioned on 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 decompositions. 

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