A Bayesian approach to sparse plus low rank network identification

We consider the problem of modeling multivariate stochastic processes with parsimonious dynamical models which can be represented with a sparse dynamic network with few latent nodes. This structure translates into a sparse plus low rank model. In this paper, we propose a Bayesian approach to identify such models.