In probability theory and statistics, a **covariance matrix** (also known as **dispersion matrix** or **variance covariance matrix**) is a matrix whose element in the *i*, *j* position is the covariance between the *i* th and *j* th elements of a random vector (that is, of a vector of random variables). Each element of the vector is a scalar random variable, either with a finite number of observed empirical values or with a finite or infinite number of potential values specified by a theoretical joint probability distribution of all the random variables.

Intuitively, the covariance matrix generalizes the notion of variance to multiple dimensions. As an example, the variation in a collection of random points in two-dimensional space cannot be characterized fully by a single number, nor would the variances in the *x* and *y* directions contain all of the necessary information; a 2×2 matrix would be necessary to fully characterize the two-dimensional variation.

Read more about Covariance Matrix: Definition, Conflicting Nomenclatures and Notations, Properties, As A Linear Operator, Which Matrices Are Covariance Matrices?, How To Find A Valid Covariance Matrix, Complex Random Vectors, Estimation, As A Parameter of A Distribution

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### Famous quotes containing the word matrix:

“As all historians know, the past is a great darkness, and filled with echoes. Voices may reach us from it; but what they say to us is imbued with the obscurity of the *matrix* out of which they come; and try as we may, we cannot always decipher them precisely in the clearer light of our day.”

—Margaret Atwood (b. 1939)