Calibration of probabilistic forecasts

Proper scoring rules, skill scores etc

June 17, 2015 — November 15, 2023

model selection
regression
signal processing
statistics
stochastic processes
time series
Figure 1

Intuitively speaking, ensuring that if our prediction is 80% certain, that we are wrong as close to 20% of the time as possible. But also the same for all other certainties.

Placeholder.

I do not know much about this but I could probably start from the compact lit review in Gneiting and Raftery (2007).

1 References

Gneiting, and Raftery. 2007. “Strictly Proper Scoring Rules, Prediction, and Estimation.” Journal of the American Statistical Association.
Henzi, Shen, Law, et al. 2023. “Invariant Probabilistic Prediction.”
Pacchiardi, and Dutta. 2022. “Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators.” arXiv:2104.03889 [Stat].
Székely, and Rizzo. 2013. “Energy Statistics: A Class of Statistics Based on Distances.” Journal of Statistical Planning and Inference.