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

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

Create Gaussian node

Methods

__init__(mu, Lambda, **kwargs) Create Gaussian node
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(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.
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