Some estimators are highly sensitive to outliers, notably estimation of covariance matrices. Random variables whose covariance is zero are called uncorrelated. Brownian covariance is motivated by generalization of the notion of covariance to stochastic processes. Operators are also involved in probability theory, such as expectation, variance, and covariance. A third concept related to covariance and contravariance is invariance. The variance is the covariance of the data with itself. Tensors are objects in multilinear algebra that can have aspects of both covariance and contravariance. Let "X" be a mixture of "G" "p"-dimensional Gaussian distributions with common covariance. See also Covariance and contravariance of vectors. In the matrix diagonal there are variances, i.e., the covariance of each element with itself. Furthermore, let the covariance matrix be denoted by formula 149. Here the covariance matrix is formula 31. This results in formula 175 which is the trace of the covariance matrix. CMA-ES stands for "covariance matrix adaptation evolution strategy". Also let formula 83 be the covariance matrix of formula 4.