scoringrules.logs_beta

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scoringrules.logs_beta#

scoringrules.logs_beta(obs: ArrayLike, a: ArrayLike, b: ArrayLike, lower: ArrayLike = 0.0, upper: ArrayLike = 1.0, *, backend: Backend = None) → ArrayLike#

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

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

Parameters:
obsarray_like

The observed values.

aarray_like

First shape parameter of the forecast beta distribution.

barray_like

Second shape parameter of the forecast beta distribution.

lowerarray_like

Lower bound of the forecast beta distribution.

upperarray_like

Upper bound of the forecast beta distribution.

backendstr

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

Returns:
scorearray_like

The LS between Beta(a, b) and obs.

Examples

>>> import scoringrules as sr
>>> sr.logs_beta(0.3, 0.7, 1.1)
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