redback.likelihoods.GaussianLikelihoodQuadratureNoise

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

Bases: GaussianLikelihood

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

A general Gaussian likelihood - the parameters are inferred from the arguments of function

Parameters:

• y (np.ndarray) – The y values.

• sigma_i (Union[float, None, np.ndarray]) – The standard deviation of the noise. This is part of the full noise. The sigma used in the likelihood is sigma = sqrt(sigma_i^2 + sigma^2)

• function (callable) – 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).

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

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

Call self as a function.

Methods

__init__(x, y, sigma_i, function[, kwargs])	A general Gaussian likelihood - the parameters are inferred from the arguments of function
log_likelihood()	return: The log-likelihood.
noise_log_likelihood()	return: The noise log-likelihood, i.e. the log-likelihood assuming the signal is just noise.

Attributes

full_sigma	The standard deviation of the full noise :rtype: Union[float, np.ndarray]
kwargs
n	Length of the x/y-values :rtype: int
residual
sigma

property full_sigma: Union[float, ndarray]

The standard deviation of the full noise :rtype: Union[float, np.ndarray]

Type:

return

log_likelihood() → float

Returns:

The log-likelihood.

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