  We extend the recently introduced regularization/Bayesian System
Identification procedures to the estimation of time-varying systems.
Specifically, we consider an online setting, in which new data become available
at given time steps. The real-time estimation requirements imposed by this
setting are met by estimating the hyper-parameters through just one gradient
step in the marginal likelihood maximization and by exploiting the closed-form
availability of the impulse response estimate (when Gaussian prior and Gaussian
measurement noise are postulated). By relying on the use of a forgetting
factor, we propose two methods to tackle the tracking of time-varying systems.
In one of them, the forgetting factor is estimated by treating it as a
hyper-parameter of the Bayesian inference procedure.
