Pages: P685-690

In this paper, estimation of AutoRegressive (AR) and AutoRegressive Moving Average (ARMA) models is proposed in a Bayesian framework using a Gaussian Process Regression (GPR) approach. Impulse response properties of the underlying process to be modeled are exploited during the parameter estimation. As such, models of enhanced predictability can be consistently obtained, even in the case of large model orders. It is also proved that the proposed approach is strongly linked with the Prediction Error (PE) model estimation approaches, if the estimated parameters are regularized. Simulations are provided to illustrate the efficiency of the proposed approach.