MultipleLikelihoodPosterior

class cuqi.distribution.MultipleLikelihoodPosterior(*densities)

A posterior distribution with multiple likelihoods and a single prior.

Parameters:

densities (Distribution or Likelihood) – The densities that make up the Posterior. Must include at least three densities. For a simple Likelihood and prior use Posterior instead.

Notes

This acts like a regular distribution with a single parameter vector. Behind-the-scenes it is a joint posterior distribution with multiple likelihoods and a single prior. This is mostly intended to be used by samplers that are not able to handle joint distributions. See JointDistribution for more details on the joint distribution.

__init__(*densities)

Create a joint distribution from the given densities.

Methods

__init__(*densities)	Create a joint distribution from the given densities.
disable_FD()	Disable finite difference approximation for logd gradient.
enable_FD([epsilon])	Enable finite difference approximation for logd gradient.
get_conditioning_variables()	Return the conditioning variables of this distribution (if any).
get_density(name)	Return a density with the given name.
get_mutable_variables()	Return any public variable that is mutable (attribute or property) except those in the ignore_vars list
get_parameter_names()	Returns the parameter names of the joint distribution.
gradient(*args, **kwargs)	Return the gradient of the un-normalized log density function.
logd(*args, **kwargs)	Evaluate the un-normalized log density function.
logpdf(*args, **kwargs)	Evaluate the log probability density function of the distribution.
pdf(x)	Evaluate the log probability density function of the distribution.
sample([N])	Sample from the distribution.
to_likelihood(data)	Convert conditional distribution to a likelihood function given observed data

Attributes

FD_enabled	Returns True if finite difference approximation of the logd gradient is enabled.
FD_epsilon	Spacing for the finite difference approximation of the logd gradient.
dim	Return the dimension of the distribution.
geometry	The geometry of the distribution.
is_cond	Returns True if instance (self) is a conditional distribution.
likelihoods	Return the likelihoods of the posterior.
models	Return the forward models that make up the posterior.
name	Name of the random variable associated with the density.
prior	Return the prior distribution of the posterior.