bilby.core.likelihood.ExponentialLikelihood

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

Bases: Analytical1DLikelihood

__init__(x, y, func, **kwargs)[source]

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()

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 positive.

property func

Make func read-only

property function_keys

Makes function_keys read_only

log_likelihood()[source]
Returns:
float
log_likelihood_ratio()[source]

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()[source]
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 positive.