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| Each cell identified by ''(i,j)'' carries a value of the covariance between term ''i'' and term ''j''. The diagonal is therefore each term's variance. If the terms are independently distributed, their covariances are 0, and the matrix is fully specified as the [[LinearAlgebra/SpecialMatrices#Diagonal_Matrices|diagonal matrix]] of variances. | Each cell identified by ''(i,j)'' carries a value of the covariance between term ''i'' and term ''j''. The diagonal is therefore each term's variance. If the terms are independently distributed, their covariances are 0, and the matrix is fully specified as the [[LinearAlgebra/Diagonalization|diagonal matrix]] of variances. |
Covariance Matrices
Covariance matrices are specially constricted matrices that are useful for various procedures.
Description
The matrix is usually notated as Σ.
Each cell identified by (i,j) carries a value of the covariance between term i and term j. The diagonal is therefore each term's variance. If the terms are independently distributed, their covariances are 0, and the matrix is fully specified as the diagonal matrix of variances.
Precision Matrices
The inverse of a covariance matrix, notated as Σ-1, is called a precision matrix.