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Definition of Correlation Functions and Standard Deviation

Our estimate of the true average \mu_{X} is the sample mean \langle X_m \rangle \begin{equation*} \mu_{X}^{\phantom X} \approx X_m=\frac{1}{mn}\sum_{\alpha=1}^m\sum_{k=1}^n x_{\alpha,k}. \end{equation*}

We can then use Eq. (9) \begin{equation*} \sigma^2_m=\frac{1}{mn^2}\sum_{\alpha=1}^m\sum_{kl=1}^n (x_{\alpha,k}-\langle X_m \rangle)(x_{\alpha,l}-\langle X_m \rangle), \end{equation*} and rewrite it as \begin{equation*} \sigma^2_m=\frac{\sigma^2}{n}+\frac{2}{mn^2}\sum_{\alpha=1}^m\sum_{k < l}^n (x_{\alpha,k}-\langle X_m \rangle)(x_{\alpha,l}-\langle X_m \rangle), \end{equation*} where the first term is the sample variance of all mn experiments divided by n and the last term is nothing but the covariance which arises when k\ne l .