bayespy.nodes.Multinomial

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

Node for multinomial random variables.

Assume there are K categories and N trials each of which leads a success for exactly one of the categories. Given the probabilities p_0,\ldots,p_{K-1} for the categories, multinomial distribution is gives the probability of any combination of numbers of successes for the categories.

The node models the number of successes x_k \in \{0, \ldots, n\} in n trials with probability p_k for success in K categories.

\mathrm{Multinomial}(\mathbf{x}| N, \mathbf{p}) = \frac{N!}{x_0!\cdots
x_{K-1}!} p_0^{x_0} \cdots p_{K-1}^{x_{K-1}}

Parameters
nscalar or array

N, number of trials

pDirichlet-like node or (…,K)-array

\mathbf{p}, probabilities of successes for the categories

__init__(n, p, **kwargs)[source]

Create Multinomial node.

Methods

__init__(n, p, **kwargs)

Create Multinomial 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