An efficient Bayes solution to AR signal modelling for short sequences

A Bayesian approach to autoregressive (AR) signal modelling is proposed. In contrast to previous research, the exact posterior density of the model parameters is utilized and minimum mean square estimates (MMSE) are evaluated. To compute the estimates, a numerically efficient procedure is presented which can be viewed as an alternative to multidimensional optimization. Our approach can be used to investigate many signal characteristics such as the signal's spectrum, marginal densities for prediction or even model selection. Simulation results confirm our expectations and illustrate the improvement over the classic, maximum conditional likelihood (MCL) approach to AR signal modelling.<>