redback.likelihoods.GaussianLikelihoodWithSystematicNoise
- class redback.likelihoods.GaussianLikelihoodWithSystematicNoise(*args: Any, **kwargs: Any)[source]
Bases:
GaussianLikelihood
- __init__(x: ndarray, y: ndarray, sigma_i: Union[float, None, ndarray], function: callable, kwargs: Optional[dict] = None) None [source]
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
- return:
The log-likelihood.
- return:
The noise log-likelihood, i.e. the log-likelihood assuming the signal is just noise.
Attributes
The standard deviation of the full noise :rtype: Union[float, np.ndarray]
kwargs
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
- property n: int
Length of the x/y-values :rtype: int
- Type:
return