scoringrules.logs_lognormal

Contents

scoringrules.logs_lognormal#

scoringrules.logs_lognormal(obs: ArrayLike, mulog: ArrayLike, sigmalog: ArrayLike, backend: Backend = None) → ArrayLike#

Compute the logarithmic score (LS) for the log-normal distribution.

This score is equivalent to the negative log likelihood of the log-normal distribution

Parameters:
obsarray_like

The observed values.

mulogarray_like

Mean of the normal underlying distribution.

sigmalogarray_like

Standard deviation of the underlying normal distribution.

Returns:
scorearray_like

The LS between Lognormal(mu, sigma) and obs.

Examples

>>> import scoringrules as sr
>>> sr.logs_lognormal(0.0, 0.4, 0.1)
Contents