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) = ..

__init__($self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

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.