My terminology might be a bit sloppy. I apologize in advance.
I'm reading on multivariate probabilistic distributions, particularly on Gaussian normal distribution (in the context of probabilistic robotics). I've encountered the generalization of the covariance $\mu$ to the covariance matrix $\Sigma $, and I'd like to get an intuitive understanding of this generalization. The diagonal terms of the matrix are the covariances (how much they differ from the mean) of the corresponding variable, but what about the off-diagonal terms?