Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2022-12
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Language
en
Pages
29
1043-1071
1043-1071
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Bayesian Analysis, Volume 17, issue 4
Abstract
Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predictive performance is heterogeneous in inputs, and we can further improve the stacked mixture with a hierarchical model. We generalize stacking to Bayesian hierarchical stacking. The model weights are varying as a function of data, partially-pooled, and inferred using Bayesian inference. We further incorporate discrete and continuous inputs, other structured priors, and time series and longitudinal data. To verify the performance gain of the proposed method, we derive theory bounds, and demonstrate on several applied problems.Description
Publisher Copyright: © 2022 International Society for Bayesian Analysis
Keywords
Bayesian hierarchical modeling, Conditional prediction, Covariate shift, Model averaging, Prior construction, Stacking
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Citation
Yao, Y, Pirš, G, Vehtari, A & Gelman, A 2022, ' Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful ', Bayesian Analysis, vol. 17, no. 4, pp. 1043-1071 . https://doi.org/10.1214/21-BA1287