bayespy.nodes.Categorical

class bayespy.nodes.Categorical(p, **kwargs)[source]

Node for categorical random variables.

The node models a categorical random variable x \in \{0,\ldots,K-1\} with prior probabilities \{p_0, \ldots, p_{K-1}\} for each category:

p(x=k) = p_k \quad \text{for } k\in \{0,\ldots,K-1\}.

Parameters
pDirichlet-like node or (…,K)-array

Probabilities for each category

__init__(p, **kwargs)[source]

Create Categorical node.

Methods

__init__(p, **kwargs)

Create Categorical 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(*args)

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.

save(filename)

set_parameters(x)

Set the parameters of the VB distribution.

set_plotter(plotter)

show()

Print the distribution using standard parameterization.

unobserve()

update([annealing])

Attributes

dims

plates

plates_multiplier

Plate multiplier is applied to messages to parents