bilby.core.likelihood.ExponentialLikelihood

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

Bases: Analytical1DLikelihood

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

An exponential likelihood function.

Parameters:

x, y: array_like

The data to analyse

func:

The python function to fit to the data. Note, this must take the dependent variable as its first argument. The other arguments will require a prior and will be sampled over (unless a fixed value is given). The model should return the expected mean of the exponential distribution for each data point.

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__(x, y, func, **kwargs)	An exponential likelihood function.
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
parameters
x	The independent variable.
y	Property assures that y-value is positive.

property func

Make func read-only

property function_keys

Makes function_keys read_only

log_likelihood(parameters=None)

Returns:

float

log_likelihood_ratio(parameters=None)

Difference between log likelihood and noise log likelihood

Returns:

float

model_parameters(parameters=None)

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=None)

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 positive.