bilby.core.likelihood.JointLikelihood

class bilby.core.likelihood.JointLikelihood(*likelihoods)[source]

Bases: Likelihood

__init__(*likelihoods)[source]

A likelihood for combining pre-defined likelihoods. The parameters dict is automagically combined through parameters dicts of the given likelihoods. If parameters have different values have initially different values across different likelihoods, the value of the last given likelihood is chosen. This does not matter when using the JointLikelihood for sampling, because the parameters will be set consistently

Parameters:
*likelihoods: bilby.core.likelihood.Likelihood

likelihoods to be combined parsed as arguments

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__(*likelihoods)

A likelihood for combining pre-defined likelihoods.

log_likelihood()

This is just the sum of the log likelihoods of all parts of the joint likelihood

log_likelihood_ratio()

Difference between log likelihood and noise log likelihood

noise_log_likelihood()

This is just the sum of the noise likelihoods of all parts of the joint likelihood

Attributes

likelihoods

The list of likelihoods

marginalized_parameters

meta_data

property likelihoods

The list of likelihoods

log_likelihood()[source]

This is just the sum of the log likelihoods of all parts of the joint likelihood

log_likelihood_ratio()[source]

Difference between log likelihood and noise log likelihood

Returns:
float
noise_log_likelihood()[source]

This is just the sum of the noise likelihoods of all parts of the joint likelihood