Classical vs. Bayesian methods for linear system identification: Point estimators and confidence sets

This paper compares classical parametric methods with recently developed regularization/Bayesian methods for system identification. A Full Bayes solution is considered together with the approximation based on the Empirical Bayes paradigm. Results regarding point estimators for the impulse response as well as for confidence regions are reported.