bayespy.nodes.Gaussian¶
- class bayespy.nodes.Gaussian(mu, Lambda, **kwargs)[source]¶
Node for Gaussian variables.
The node represents a -dimensional vector from the Gaussian distribution:
where is the mean vector and is the precision matrix (i.e., inverse of the covariance matrix).
- 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
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
()Return parameters of the VB distribution.
Computes the Riemannian/natural gradient.
get_shape
(ind)Return True if the node has a plotter
initialize_from_parameters
(mu, Lambda)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) ]
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 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
update
([annealing])Attributes
Plate multiplier is applied to messages to parents