bayespy.nodes.GaussianGamma

class bayespy.nodes.GaussianGamma(*args, **kwargs)

Node for Gaussian-gamma (isotropic) random variables.

The prior:

The posterior approximation has the same Gaussian-gamma 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_gaussian_location()	Return the mean and variance of the distribution
get_gaussian_mean_and_variance()	Return the mean and variance of the distribution
get_gradient(rg)	Computes gradient with respect to the natural parameters.
get_marginal_logpdf([gaussian, gamma])	Get the (marginal) log pdf of a subset of the variables
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
plotmatrix()	Creates a matrix of marginal plots.
random()	Draw a random sample from the distribution.
rotate(R[, inv, logdet, debug])
save(filename)
set_parameters(x)	Set the parameters of the VB distribution.
set_plotter(plotter)
show()	Print the distribution using standard parameterization.
translate(b[, debug])
unobserve()
update([annealing])

Attributes

dims
plates
plates_multiplier	Plate multiplier is applied to messages to parents