scoringrules.logs_tt

scoringrules.logs_tt(obs: ArrayLike, df: ArrayLike, location: ArrayLike = 0.0, scale: ArrayLike = 1.0, lower: ArrayLike = -inf, upper: ArrayLike = inf, *, backend: Backend = None) → ArrayLike

Compute the logarithmic score (LS) for the truncated Student’s t distribution.

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

Parameters:

obsarray_like

The observed values.

dfarray_like

Degrees of freedom parameter of the forecast distribution.

locationarray_like

Location parameter of the forecast distribution.

scalearray_like

Scale parameter of the forecast distribution.

lowerarray_like

Lower boundary of the truncated forecast distribution.

upperarray_like

Upper boundary of the truncated forecast distribution.

Returns:

scorearray_like

The LS between tt(df, location, scale, lower, upper) and obs.

Examples

>>> import scoringrules as sr
>>> sr.logs_tt(0.0, 2.0, 0.1, 0.4, -1.0, 1.0)