**"FunctionMatrixCovariance"**

In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between each pair of elements of a given random vector.

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.

Any covariance matrix is symmetric and positive semi-definite and its main diagonal contains variances (i.e., the covariance of each element with itself).

The covariance matrix of a random vector `X` is typically denoted by `Kxx`, `Σ` or `S`.

~wikipedia.

**method cov(M, bias)**

Estimate Covariance matrix with provided data.

Namespace types: matrix<float>

Parameters:

**M (matrix<float>)**: `matrix<float>` Matrix with vectors in column order.

**bias (bool)**

Returns: Covariance matrix of provided vectors.

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en.wikipedia.org/wiki/Covariance_matrix

numpy.org/doc/stable...rated/numpy.cov.html