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A convenient prior for covariance matrices in PyMC
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README.rst

Date: 2 June 2009
Author: Anand Patil
Contact: anand.prabhakar.patil@gmail.com
Web site:github.com/onyin/covariance-prior
Copyright: Anand Patil, 2009.
License:MIT License, see LICENSE

This package provides a convenient prior for covariance matrices in PyMC. To use it, do the following:

c,o = covariance(name, v)

where v is any vector-valued variable whose elements will always be positive. o will be an OrthogonalBasis object and c will be a deterministic returning a covariance matrix whose eigenvalues are v and whose eigenvectors are c.

OrthogonalBasis objects are matrix-valued stochastics whose columns form an orthonormal basis, but which are otherwise indifferent to their values. They are handled by GivensStepper step methods, which propose Givens rotations in randomly-selected planes.

By default, OrthogonalBasis objects' logp functions enforce orthogonality. You can skip this check for speed if you like by setting constrain=False.

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