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NotesParametersReturns
wishart_gen._cholesky_logdet(self, scale)

Notes

This computation of logdet is equivalent to np.linalg.slogdet(scale). It is ~2x faster though.

Parameters

scale : ndarray

Scale matrix.

Returns

c_decomp : ndarray

The Cholesky decomposition of scale.

logdet : scalar

The log of the determinant of scale.

Compute Cholesky decomposition and determine (log(det(scale)).

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/stats/_multivariate.py#2329
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