redback.likelihoods.GaussianLikelihoodUniformXErrors

class redback.likelihoods.GaussianLikelihoodUniformXErrors(*args: Any, **kwargs: Any)

Bases: GaussianLikelihood

__init__(x: ndarray, y: ndarray, sigma: Union[float, None, ndarray], bin_size: Union[float, None, ndarray], function: callable, kwargs: Optional[dict] = None, priors=None, fiducial_parameters=None) → None

A general Gaussian likelihood with uniform errors in x- the parameters are inferred from the arguments of function. Takes into account the X errors with a Uniform likelihood between the bin high and bin low values. Note that the prior for the true x values must be uniform in this range!

Parameters:

• x (np.ndarray) – The x values.

• y (np.ndarray) – The y values.

• sigma (Union[float, None, np.ndarray]) – The standard deviation of the noise.

• bin_size (Union[float, None, np.ndarray]) – The bin sizes.

• function (callable) – The python function to fit to the data. Note, this must take the dependent variable as its first argument. The other arguments are will require a prior and will be sampled over (unless a fixed value is given).

• kwargs (dict) – Any additional keywords for ‘function’.

• priors – The priors for the parameters. Default to None if not provided.

Only necessary if using maximum likelihood estimation functionality. :type priors: Union[dict, None] :param fiducial_parameters: The starting guesses for model parameters to use in the optimization for maximum likelihood estimation. Default to None if not provided. :type fiducial_parameters: Union[dict, None]

__call__(*args: Any, **kwargs: Any) → Any

Call self as a function.

Methods

__init__(x, y, sigma, bin_size, function[, ...])	A general Gaussian likelihood with uniform errors in x- the parameters are inferred from the arguments of function.
find_maximum_likelihood_parameters([...])	Estimate the maximum likelihood
get_bounds_from_priors(priors)
get_parameter_dictionary_from_list(...)
get_parameter_list_from_dictionary(...)
lnlike_scipy_maximize(parameter_list)
log_likelihood()	return: The log-likelihood.
log_likelihood_x()	return: The log-likelihood due to x-errors.
log_likelihood_y()	return: The log-likelihood due to y-errors.
noise_log_likelihood()	return: The noise log-likelihood, i.e. the log-likelihood assuming the signal is just noise.

Attributes

kwargs
n	Length of the x/y-values :rtype: int
parameters_to_be_updated
residual
sigma

find_maximum_likelihood_parameters(iterations=5, maximization_kwargs=None, method='Nelder-Mead', break_threshold=0.001)

Estimate the maximum likelihood

Parameters:

• iterations – Iterations to run the minimizer for before stopping. Default is 5.

• maximization_kwargs – Any extra keyword arguments passed to the scipy minimize function

• method – Minimize method to use. Default is ‘Nelder-Mead’

• break_threshold – The threshold for the difference in log likelihood to break the loop. Default is 1e-3.

Returns:

Dictionary of maximum likelihood parameters

log_likelihood() → float

Returns:

The log-likelihood.

Return type:

float

log_likelihood_x() → float

Returns:

The log-likelihood due to x-errors.

Return type:

float

log_likelihood_y() → float

Returns:

The log-likelihood due to y-errors.

Return type:

float

property n: int

Length of the x/y-values :rtype: int

Type:

return

noise_log_likelihood() → float

Returns:

The noise log-likelihood, i.e. the log-likelihood assuming the signal is just noise.

Return type:

float