**This note describes the covariance method for finding the parameters needed to fit an all-pole model to a finite sequence of samples obtained from a deterministic signal.**

The covariance method is a technique for fitting an all-pole model to a deterministic signal that is assumed to be auto regressive, but where knowledge about the signal is limited to a sequence of *N* samples, *x*[0] through *x*[*N* –1]. This method is an alternative to the autocorrelation method described in Note 70. Rather than optimizing the total error over all non-negative *n* and assuming that *x*[*n*] = 0 for *n* outside the interval [0, *n* –1], as the autocorrelation method does, the covariance method is based ...

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