bayespy.nodes.Wishart

class bayespy.nodes.Wishart(n, V, **kwargs)

Node for Wishart random variables.

The random variable is a positive-definite symmetric matrix.

Parameters:

• n (scalar or array) – , degrees of freedom, .

• V (Wishart-like node or (...,D,D)-array) – , scale matrix.

__init__(n, V, **kwargs)

Create Wishart node.

Methods

__init__(n, V, **kwargs)	Create Wishart node.
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)
scale(scalar, **kwargs)
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