bayespy.nodes.Binomial

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

Node for binomial random variables.

The node models the number of successes x \in \{0, \ldots, n\} in n trials with probability p for success:

x \sim \mathrm{Binomial}(n, p).

Parameters
nscalar or array

Number of trials

pbeta-like node or scalar or array

Probability of a success in a trial

Examples

>>> import warnings
>>> warnings.filterwarnings('ignore', category=RuntimeWarning)
>>> from bayespy.nodes import Binomial, Beta
>>> p = Beta([1e-3, 1e-3])
>>> x = Binomial(10, p)
>>> x.observe(7)
>>> p.update()
>>> import bayespy.plot as bpplt
>>> import numpy as np
>>> bpplt.pdf(p, np.linspace(0, 1, num=100))
[<matplotlib.lines.Line2D object at 0x...>]
__init__(n, p, **kwargs)[source]

Create binomial node

Methods

__init__(n, p, **kwargs)

Create binomial 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