bayespy.nodes.Gaussian

class bayespy.nodes.Gaussian(mu, Lambda, **kwargs)[source]

Node for Gaussian variables.

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

\mathbf{x} &\sim \mathcal{N}(\boldsymbol{\mu}, \mathbf{\Lambda}),

where \boldsymbol{\mu} is the mean vector and \mathbf{\Lambda} is the precision matrix (i.e., inverse of the covariance matrix).

\mathbf{x},\boldsymbol{\mu} \in \mathbb{R}^{D}, \quad \mathbf{\Lambda} \in \mathbb{R}^{D \times D}, \quad \mathbf{\Lambda} \text{ symmetric positive definite}

Parameters:

  • mu (Gaussian-like node or GaussianGamma-like node or GaussianWishart-like node or array) – Mean vector

  • Lambda (Wishart-like node or array) – Precision matrix

See also

Wishart, GaussianARD, GaussianWishart, GaussianGamma

__init__(mu, Lambda, **kwargs)[source]

Create Gaussian node

Methods

__init__(mu, Lambda, **kwargs)

Create Gaussian 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(mu, Lambda)

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.

observe_limits([minimum, maximum])

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, Q])

rotate_matrix(R1, R2[, inv1, logdet1, inv2, ...])

The vector is reshaped into a matrix by stacking the row vectors.

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