A Divergence-Based Method for Weighting and Averaging Model Predictions
This paper uses a minimum divergence framework to introduce a new way of calculating model weights that can be used to average probabilistic predictions from statistical and machine learning models. The method is general and can be applied regardless of whether the models under consideration are fit to data using frequentist, Bayesian, or some other fitting method. The proposed method is motivated in two different ways and is shown empirically to perform better than or on a par with standard model averaging methods, including model stacking and model averaging that relies on Akaike-style negative exponentiated model weighting, especially when the sample size is small. Our theoretical anal...
| Authors | Olav Benjamin Vassend |
|---|---|
| Primary category | stat.ML |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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