scoringrules.logs_poisson

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scoringrules.logs_poisson#

scoringrules.logs_poisson(obs: ArrayLike, mean: ArrayLike, *, backend: Backend = None) → ArrayLike#

Compute the logarithmic score (LS) for the Poisson distribution.

This score is equivalent to the negative log likelihood of the Poisson distribution.

Parameters:
obsarray_like

The observed values.

meanarray_like

Mean parameter of the forecast poisson distribution.

backendstr

The name of the backend used for computations. Defaults to ‘numba’ if available, else ‘numpy’.

Returns:
scorearray_like

The LS between Pois(mean) and obs.

Examples

>>> import scoringrules as sr
>>> sr.logs_poisson(1, 2)
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