  We consider an on-line system identification setting, in which new data
become available at given time steps. In order to meet real-time estimation
requirements, we propose a tailored Bayesian system identification procedure,
in which the hyper-parameters are still updated through Marginal Likelihood
maximization, but after only one iteration of a suitable iterative optimization
algorithm. Both gradient methods and the EM algorithm are considered for the
Marginal Likelihood optimization. We compare this "1-step" procedure with the
standard one, in which the optimization method is run until convergence to a
local minimum. The experiments we perform confirm the effectiveness of the
approach we propose.

Systems and Control;Machine Learning;Applications;Machine Learning