bayespy.nodes.GaussianARD

class bayespy.nodes.GaussianARD(mu, alpha, ndim=None, shape=None, **kwargs)

Node for Gaussian variables with ARD prior.

The node represents a -dimensional vector from the Gaussian distribution:

where is the mean vector and is the diagonal precision matrix (i.e., inverse of the covariance matrix).

Note: The form of the posterior approximation is a Gaussian distribution with full covariance matrix instead of a diagonal matrix.

Parameters

muGaussian-like node or GaussianGamma-like node or array Mean vector

alphagamma-like node or array

Diagonal elements of the precision matrix

See also

Gamma, Gaussian, GaussianGamma, GaussianWishart

__init__(mu, alpha, ndim=None, shape=None, **kwargs)

Create GaussianARD node.

Methods

__init__(mu, alpha[, ndim, shape])	Create GaussianARD 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_mean_and_covariance(mu, Cov)
initialize_from_parameters(mu, alpha)
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.
rotate(R[, inv, logdet, axis, Q, subset, debug])
rotate_plates(Q[, plate_axis])	Approximate rotation of a plate axis.
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])	Transforms the current posterior by adding a bias to the mean
unobserve()
update([annealing])

Attributes

dims
plates
plates_multiplier	Plate multiplier is applied to messages to parents