scoringrules.logs_2pnormal

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scoringrules.logs_2pnormal#

scoringrules.logs_2pnormal(obs: ArrayLike, scale1: ArrayLike, scale2: ArrayLike, location: ArrayLike, *, backend: Backend = None) → ArrayLike#

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

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

Parameters:
obsarray_like

The observed values.

scale1array_like

Scale parameter of the lower half of the forecast two-piece normal distribution.

scale2array_like

Scale parameter of the upper half of the forecast two-piece normal distribution.

locationarray_like

Location parameter of the forecast two-piece normal distribution.

backendstr

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

Returns:
scorearray_like

The LS between 2pNormal(scale1, scale2, location) and obs.

Examples

>>> import scoringrules as sr
>>> sr.logs_2pnormal(0.0, 0.4, 2.0, 0.1)
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