Bayesian and nonparametric methods for system identification and model selection

System Identification has been developed, by and large, following the classical parametric approach. In this tutorial we shall discuss how Bayesian statistics and regularization theory can be employed to tackle the system identification problem from a nonparametric (or semi-parametric) point of view. The present paper provides an introduction to the use of Bayesian techniques for smoothness and sparseness, which turn out to be flexible means to face the bias/variance dilemma and to perform model selection.