6 papers
Data-driven Link Prediction over Graphical Models
Daniele Alpago, Mattia Zorzi, Augusto Ferrante
The positive link prediction (PLP) problem is formulated in a system identification framework: we consider dynamic graphical models for auto-regressive moving-average (ARMA) Gaussi…
Link Prediction: A Graphical Model Approach
Daniele Alpago, Mattia Zorzi, Augusto Ferrante
We consider the problem of link prediction in networks whose edge structure may vary (sufficiently slowly) over time. This problem, with applications in many important areas includ…
An Extended Kalman Filter for Data-enabled Predictive Control
Daniele Alpago, Florian Dorfler, John Lygeros
The literature dealing with data-driven analysis and control problems has significantly grown in the recent years. Most of the recent literature deals with linear time-invariant sy…
Optimal steering for non-Markovian Gaussian processes
Daniele Alpago, Yongxin Chen, Tryphon Georgiou +1
At present, the problem to steer a non-Markovian process with minimum energy between specified end-point marginal distributions remains unsolved. Herein, we consider the special ca…
A Scalable Strategy for the Identification of Latent-variable Graphical Models
Daniele Alpago, Mattia Zorzi, Augusto Ferrante
In this paper we propose an identification method for latent-variable graphical models associated to autoregressive (AR) Gaussian stationary processes. The identification procedure…
Identification of Sparse Reciprocal Graphical Models
Daniele Alpago, Mattia Zorzi, Augusto Ferrante
In this paper we propose an identification procedure of a sparse graphical model associated to a Gaussian stationary stochastic process. The identification paradigm exploits the ap…