Definition of Correlation Functions and Standard Deviation, sample variance

The sample variance of the \( mn \) experiments can now be written in terms of the autocorrelation function $$ \begin{equation} \sigma_m^2=\frac{\sigma^2}{n}+\frac{2}{n}\cdot\sigma^2\sum_{d=1}^{n-1} \frac{f_d}{\sigma^2}=\left(1+2\sum_{d=1}^{n-1}\kappa_d\right)\frac{1}{n}\sigma^2=\frac{\tau}{n}\cdot\sigma^2 \tag{12} \end{equation} $$ and we see that \( \sigma_m \) can be expressed in terms of the uncorrelated sample variance times a correction factor \( \tau \) which accounts for the correlation between measurements. We call this correction factor the autocorrelation time $$ \begin{equation} \tau = 1+2\sum_{d=1}^{n-1}\kappa_d \tag{13} \end{equation} $$ For a correlation free experiment, \( \tau \) equals 1.