# 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

__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 this node and the children Computes gradient with respect to the natural parameters. 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 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) Print the distribution using standard parameterization. translate(b[, debug]) Transforms the current posterior by adding a bias to the mean update([annealing])

Attributes

 dims plates plates_multiplier Plate multiplier is applied to messages to parents