bayespy.nodes.GaussianWishart

class bayespy.nodes.GaussianWishart(*args, **kwargs)[source]

Node for Gaussian-Wishart random variables.

The prior:

p(x, \Lambda| \mu, \alpha, V, n) p(x|\Lambda, \mu, \alpha) = \mathcal(N)(x | \mu, \alpha^{-1} Lambda^{-1}) p(\Lambda|V, n) = \mathcal(W)(\Lambda | n, V)

The posterior approximation q(x, \Lambda) has the same Gaussian-Wishart form.

Currently, supports only vector variables.

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)

add_plate_axis(to_plate)

broadcasting_multiplier(plates, *args)

delete()

Delete this node and the children

get_gradient(rg)

Computes gradient with respect to the natural parameters.

get_mask()

get_moments()

get_parameters()

Return parameters of the VB distribution.

get_pdf_nodes()

get_riemannian_gradient()

Computes the Riemannian/natural gradient.

get_shape(ind)

has_plotter()

Return True if the node has a plotter

initialize_from_parameters(*args)

initialize_from_prior()

initialize_from_random()

Set the variable to a random sample from the current distribution.

initialize_from_value(x, *args)

load(filename)

logpdf(X[, mask])

Compute the log probability density function Q(X) of this node.

lower_bound_contribution([gradient, ...])

Compute E[ log p(X|parents) - log q(X) ]

lowerbound()

move_plates(from_plate, to_plate)

observe(x, *args[, mask])

Fix moments, compute f and propagate mask.

pdf(X[, mask])

Compute the probability density function of this node.

plot([fig])

Plot the node distribution using the plotter of the node

random()

Draw a random sample from the distribution.

save(filename)

set_parameters(x)

Set the parameters of the VB distribution.

set_plotter(plotter)

show()

Print the distribution using standard parameterization.

unobserve()

update([annealing])

Attributes

dims

plates

plates_multiplier

Plate multiplier is applied to messages to parents