bilby.core.likelihood.PoissonLikelihood

class bilby.core.likelihood.PoissonLikelihood(x, y, func, **kwargs)

Bases: Analytical1DLikelihood

__init__(x, y, func, **kwargs)

A general Poisson likelihood for a rate - the model parameters are inferred from the arguments of function, which provides a rate.

Parameters:

x: array_like

A dependent variable at which the Poisson rates will be calculated

y: array_like

The data to analyse - this must be a set of non-negative integers, each being the number of events within some interval.

func:

The python function providing the rate of events per interval to fit to the data. The function must be defined with the first argument being a dependent parameter (although this does not have to be used by the function if not required). The subsequent arguments will require priors and will be sampled over (unless a fixed value is given).

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__(x, y, func, **kwargs)	A general Poisson likelihood for a rate - the model parameters are inferred from the arguments of function, which provides a rate.
log_likelihood(parameters)
log_likelihood_ratio(parameters)	Difference between log likelihood and noise log likelihood
model_parameters(parameters)	This sets up the function only parameters (i.e. not sigma for the GaussianLikelihood) .
noise_log_likelihood()
residual(parameters)	Residual of the function against the data.

Attributes

func	Make func read-only
function_keys	Makes function_keys read_only
marginalized_parameters
meta_data
n	The number of data points
x	The independent variable.
y	Property assures that y-value is a positive integer.

property func

Make func read-only

property function_keys

Makes function_keys read_only

log_likelihood(parameters)

Parameters:

parameters: dict

A dictionary of the parameter names and associated values

Returns:

float

log_likelihood_ratio(parameters)

Difference between log likelihood and noise log likelihood

Parameters:

parameters: dict

A dictionary of the parameter names and associated values

Returns:

float

model_parameters(parameters)

This sets up the function only parameters (i.e. not sigma for the GaussianLikelihood)

property n

The number of data points

noise_log_likelihood()

Returns:

float

residual(parameters)

Residual of the function against the data.

property x

The independent variable. Setter assures that single numbers will be converted to arrays internally

property y

Property assures that y-value is a positive integer.