scoringrules.logs_normal

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scoringrules.logs_normal#

scoringrules.logs_normal(obs: ArrayLike, mu: ArrayLike, sigma: ArrayLike, *, negative: bool = True, backend: Backend = None) → Array#

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

This score is equivalent to the (negative) log likelihood (if negative = True)

Parameters:
obsarray_like

The observed values.

muarray_like

Mean of the forecast normal distribution.

sigmaarray_like

Standard deviation of the forecast normal distribution.

backendstr, optional

The backend used for computations.

Returns:
scorearray_like

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

Examples

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