Add CRPS for the logistic distribution #41
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frazane
reviewed
Aug 30, 2024
| where $\Phi(ω)$ and $\phi(ω)$ are respectively the CDF and PDF of the standard normal | ||
| distribution at the normalized prediction error $\omega = \frac{y - \mu}{\sigma}$. | ||
| where $F(\omega)$ is the CDF of the standard logistic distribution at the | ||
| normalized prediction error $\omega = \frac{y - \mu}{\sigma}$. | ||
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| Parameters | ||
| ---------- | ||
| observations: ArrayLike |
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The type hint suggestion can be removed in the docstrings. When mkdocstrings parses the code it will automatically infer the type from the function signature.
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(we will make sure to remove them from all other functions later)
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| Returns | ||
| ------- | ||
| crps: array_like | ||
| The CRPS between Normal(mu, sigma) and obs. | ||
| The CRPS for the Logistic(mu, sigma) forecasts given the observations. | ||
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| Examples | ||
| -------- | ||
| >>> from scoringrules import crps |
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All examples should use the main API:
import scoringrules as sr
sr.crps_logistic(...)
frazane
changed the title
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PR merged into PR for generalised truncated and censored logistic distribution |
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