bayespy.inference.vmp.nodes.wishart.WishartDistribution¶

class bayespy.inference.vmp.nodes.wishart.WishartDistribution[source]¶

Sub-classes implement distribution specific computations.

Distribution for $k imes k$ symmetric positive definite matrix.

$\Lambda \sim \mathcal{W}(n, V)$

Note: $V$ is inverse scale matrix.

$p(\Lambda | n, V) = ..$

Methods

`__init__`()
`compute_cgf_from_parents`(u_n, u_V)	CGF from parents
`compute_fixed_moments_and_f`(Lambda[, mask])	Compute u(x) and f(x) for given x.
`compute_gradient`(g, u, phi)	Compute the standard gradient with respect to the natural parameters.
`compute_logpdf`(u, phi, g, f, ndims)	Compute E[log p(X)] given E[u], E[phi], E[g] and E[f].
`compute_message_to_parent`(parent, index, ...)	Compute the message to a parent node.
`compute_moments_and_cgf`(phi[, mask])	Return moments and cgf for given natural parameters
`compute_phi_from_parents`(u_n, u_V[, mask])	Compute natural parameters
`compute_weights_to_parent`(index, weights)	Maps the mask to the plates of a parent.
`plates_from_parent`(index, plates)	Resolve the plate mapping from a parent.
`plates_to_parent`(index, plates)	Resolves the plate mapping to a parent.
`random`(*params[, plates])	Draw a random sample from the distribution.
`squeeze`(axis)	Squeeze a plate axis from the distribution

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