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()
log_likelihood_ratio()	Difference between log likelihood and noise log likelihood
noise_log_likelihood()

Attributes

func	Make func read-only
function_keys	Makes function_keys read_only
marginalized_parameters
meta_data
model_parameters	This sets up the function only parameters (i.e. not sigma for the GaussianLikelihood) .
n	The number of data points
residual	Residual of the function against the data.
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()

Returns:

float

log_likelihood_ratio()

Difference between log likelihood and noise log likelihood

Returns:

float

property model_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

property residual

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.