Built-in likelihood functions

class midas.likelihoods.GaussianLikelihood(y_data, sigma)

A class for constructing a Gaussian likelihood function.

Parameters:

• y_data (ndarray) – The measured data as a 1D array.

• sigma (ndarray) – The standard deviations corresponding to each element in y_data as a 1D array.

class midas.likelihoods.LogisticLikelihood(y_data, sigma)

A class for constructing a Logistic likelihood function.

Parameters:

• y_data (ndarray) – The measured data as a 1D array.

• sigma (ndarray) – The uncertainties corresponding to each element in y_data as a 1D array.

class midas.likelihoods.CauchyLikelihood(y_data, gamma)

A class for constructing a Cauchy likelihood function.

Parameters:

• y_data (ndarray) – The measured data as a 1D array.

• gamma (ndarray) – The uncertainties corresponding to each element in y_data as a 1D array.

class midas.likelihoods.LikelihoodFunction

An abstract base-class for likelihood function.

abstractmethod log_likelihood(predictions, **parameters)

Parameters:

• predictions (ndarray) – The model predictions of the measured data as a 1D array.

• parameters (ndarray)

Returns:

The calculated log-likelihood.

Return type:

float

class midas.likelihoods.DiagnosticLikelihood(diagnostic_model, likelihood, name)

A class enabling the calculation of the likelihood (and its derivative) for the data of a particular diagnostic.

Parameters:

• diagnostic_model (DiagnosticModel) – An instance of a diagnostic model which inherits from the DiagnosticModel base class.

• likelihood (LikelihoodFunction) – An instance of a likelihood class which inherits from the LikelihoodFunction base class.

• name (str) – A name or other identifier for the diagnostic as a string.