On the exact maximum likelihood estimation of Gaussian autoregressive processes

It is shown that the true maximum-likelihood estimates of a Gaussian zero-mean AR process can be calculated. The steps toward their obtainment with standard subroutines are calculating extrema are given. The likelihood of the data is maximized in contrast to current approximate methods. However, the computational cost is significantly higher. Also, the order p of the process is presumed known