bayespy.inference.vmp.nodes.expfamily.ExponentialFamily¶
- class bayespy.inference.vmp.nodes.expfamily.ExponentialFamily(*args, **kwargs)[source]¶
A base class for nodes using natural parameterization phi.
phi
- Sub-classes must implement the following static methods:
_compute_message_to_parent(index, u_self, *u_parents) _compute_phi_from_parents(*u_parents, mask) _compute_moments_and_cgf(phi, mask) _compute_fixed_moments_and_f(x, mask=True)
Sub-classes may need to re-implement: 1. If they manipulate plates:
_compute_weights_to_parent(index, weights) _compute_plates_to_parent(self, index, plates) _compute_plates_from_parent(self, index, plates)
Methods
__init__(*args, **kwargs)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()Return parameters of the VB distribution.
Computes the Riemannian/natural gradient.
get_shape(ind)Return True if the node has a plotter
initialize_from_parameters(*args)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) ]
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 the parameters of the VB distribution.
set_plotter(plotter)show()Print the distribution using standard parameterization.
update([annealing])Attributes
Plate multiplier is applied to messages to parents