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:

n : scalar or array

N, number of trials

p : Dirichlet-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