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Suppose $ {X_t; t \in R} $ is a wss, zero mean Gaussian random process with autocorrelation function $ R_X( \tau) ; \tau \in R$ and power spectral density $S_X(\omega); \omega \in R$. If w define the random process ${Y_t;t\in R} $ by ${Y_t = ({X_t}^2)}$

What is the autocorrelation function of $Y_t$ (in terms of $\tau$)?

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If $X_t$ and $X_{t+\tau}$ are jointly Gaussian with means $0$, variances $\sigma^2$ and covariance $c$, then $\text{Cov}(X_t^2, X_{t+\tau}^2) = 2 c^2$. One way to get this is from Isserlis's theorem.

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I think he/she asked autocorrelation, not autocovariance. – Nick X Tsui Dec 1 '15 at 21:58
I hope he/she knew how to get correlation from covariance and variances. – Robert Israel Dec 1 '15 at 22:40

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