When analyzing random processes, a good assumption is that they are ergodic in the mean and in this quantity. For an autoregressiveÂ–moving-average model, a relationship between the autoregressive 3 coefficients and this quantity is established by the Yule-Walker equations. By transforming a random process to the frequency domain and taking the conjugate of one term, one can easily compute this quantity via convolution. Wide-sense stationary processes are important because their value for this 6 quantity only depends on the difference in time, τ, which allows one to use the Wiener-Khinchin theorem to derive the (*) power spectral density of a
When analyzing random processes, a good assumption is that they are ergodic in the mean and in this quantity. For an autoregressiveÂ–moving-average model, a relationship between the autoregressive 3 coefficients and this quantity is established by the Yule-Walker equations. By transforming a random process to the frequency domain and taking the conjugate of one term, one can easily compute this quantity via convolution. Wide-sense stationary processes are important because their value for this 6 quantity only depends on the difference in time, τ, which allows one to use the Wiener-Khinchin theorem to derive the (*) power spectral density of a random process as the Fourier transform of thisquantity. For a random process X , this quantity is defined as the expectation value of quantity X (t1) Ã— X (t2). 9 For 10 points, name this quantity symbolized RX X that is defined as the cross-correlation of a signal withitself.
When analyzing random processes, a good assumption is that they are ergodic in the mean and in this quantity.
autocorrelation [prompt on: cross-correlation]