Using Bayesian Model Averaging to Calibrate Forecast Ensembles¶
Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133(5), 1155-1174.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bayesian Cue Integration
- In ensemble weather forecasting it is Bayesian model averaging weighted by historical performance.
This sourceApplies Bayesian model averaging, weighting ensemble members by performance, to produce calibrated probabilistic forecasts.
Borderline supportVerified against the publisher's abstract
The abstract states BMA combines ensemble forecasts into a weighted average whose weights reflect each model's relative contribution to predictive skill over the training period.
“This paper proposes a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA) ... The BMA predictive probability density function (PDF) of any quantity of interest is a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts and reflect the models' relative contributions …”
Two runs of the same check disagreed on this citation; the later verdict is shown and the citation is queued for review.
- In ensemble weather forecasting it is Bayesian model averaging weighted by historical performance.
Verification¶
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Registry ID ref:f29f9a619163 · see in the full table