bayespy.nodes.Bernoulli¶
-
class
bayespy.nodes.
Bernoulli
(p, **kwargs)[source]¶ Node for Bernoulli random variables.
The node models a binary random variable
with prior probability
for value one:
Parameters: - p : beta-like node
Probability of a successful trial
Examples
>>> import warnings >>> warnings.filterwarnings('ignore', category=RuntimeWarning) >>> from bayespy.nodes import Bernoulli, Beta >>> p = Beta([1e-3, 1e-3]) >>> z = Bernoulli(p, plates=(10,)) >>> z.observe([0, 1, 1, 1, 0, 1, 1, 1, 0, 1]) >>> 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...>]
Methods
__init__
(p, **kwargs)Create Bernoulli node. add_plate_axis
(to_plate)broadcasting_multiplier
(*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