Covariance Matrix VI

Owning Palette: Probability & Statistics VIs

Requires: Full Development System

Computes the covariance matrix of the input sequence X.

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	X is the input sequence. Each column of X represents one vector of observed samples from one variable. Each row of X represents an observation from each variable.
	covariance matrix V returns the covariance matrix of X. If X is an n-by-m 2D array, then the covariance matrix is a square m-by-m matrix.
	mean vector returns the mean of each column variable in X.

Covariance Matrix Details

Given m vectors of observed samples where the i^th column contains the variate x_i, the covariance matrix is defined as:

V_ij = cov(x_i, x_j) = (x_i – µ_i)(x_j – µ_j)

where µ_i is the mean of variate x_i. Each element V_ij of covariance matrix V is the covariance between variates x_i and x_j. The diagonal of covariance matrix V contains the standard variances of each x_i variate.

mean vector returns the computed mean of each variate as shown by the following equation:

mean vector_i = µ_i