scoringrules.logs_loglogistic#
- scoringrules.logs_loglogistic(obs: ArrayLike, mulog: ArrayLike, sigmalog: ArrayLike, backend: Backend = None) ArrayLike#
Compute the logarithmic score (LS) for the log-logistic distribution.
This score is equivalent to the negative log likelihood of the log-logistic distribution
- Parameters:
- obsarray_like
The observed values.
- mulogarray_like
Location parameter of the log-logistic distribution.
- sigmalogarray_like
Scale parameter of the log-logistic distribution.
- backendstr
The name of the backend used for computations. Defaults to ‘numba’ if available, else ‘numpy’.
- Returns:
- scorearray_like
The LS between obs and Loglogis(mulog, sigmalog).
Examples
>>> import scoringrules as sr >>> sr.logs_loglogistic(3.0, 0.1, 0.9)