Another important quantity is the so called covariance, a variant of the above defined variance. Consider again the set \{X_i\} of n stochastic variables (not necessarily uncorrelated) with the multivariate PDF P(x_1,\dots,x_n) . The covariance of two of the stochastic variables, X_i and X_j , is defined as follows:
\begin{align} \mathrm{cov}(X_i,\,X_j) &\equiv \langle (x_i-\langle x_i\rangle)(x_j-\langle x_j\rangle)\rangle \nonumber\\ &= \int\!\cdots\!\int\!(x_i-\langle x_i \rangle)(x_j-\langle x_j \rangle)\, P(x_1,\dots,x_n)\,dx_1\dots dx_n \tag{5} \end{align}with
\langle x_i\rangle = \int\!\cdots\!\int\!x_i\,P(x_1,\dots,x_n)\,dx_1\dots dx_n