# wiener filter (non-causal) calculating covariance matrices

I'm learning about the Wiener filter, and I'm working on my own implementation. I'm starting out with the non-causal filter, and I need to calculate some covariance matrices. If the signal is assumed to be a linear combination of a target signal and noise, then the signal can be modeled as: $$x[n] = y[n] + w[n]$$ where x is the observed signal, y is the target and w is noise. If I understand correctly, the weiner filter is a linear-minimum-mean-square-error estimation process. What I'm getting hung up on is that we need to compute a matrix of expected values based on the input signal x, namely $$E[xx^{T}]$$ and I know that to do this we need to apply the expectation operator to each element in the matrix $$xx^{T}$$ but I don't know how to do that computation, or what it even means.