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(*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