This paper focuses on the joint estimation of parameters and topologies of multivariate
graphical autoregressive moving-average (ARMA) processes. Since the graphical structure …

This paper focuses on the identification of graphical autoregressive models with dynamical
latent variables. The dynamical structure of latent variables is described by a matrix …

In this article, we present a novel approach to reconstruct the topology of networked linear
dynamical systems with latent nodes. The network is allowed to have directed loops and bi …

We address the problem of learning graphical models which correspond to high
dimensional autoregressive stationary stochastic processes. A graphical model describes …

We consider the problem of learning dynamic latent variable graphical models. More
precisely, given an estimate of the graphical model based on a finite data sample, we …

We address the problem to estimate a dynamic network whose edges describe Granger
causality relations and whose topology has a Kronecker structure. Such a structure arises in …

We address the problem to estimate a Kronecker graphical model corresponding to an
autoregressive Gaussian stochastic process. The latter is completely described by the power …

We study modeling and identification of stationary processes with a spectral density matrix of
low rank. Equivalently, we consider processes having an innovation of reduced dimension …

This paper investigates the problem of selecting instrumental variables relative to a target
causal influence X→ Y from observational data generated by linear non-Gaussian acyclic …

This article addresses the problem of reconstructing the topology of a network of agents
interacting via linear dynamics, while being excited by exogenous stochastic sources that …