ArticleBayesian analysis2022
Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful.
Article in Bayesian analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Behavioral thermoregulation in relation to humidity in wild Japanese macaques (Macaca fuscata yakui): the significance of semi-shade.Primates; journal of primatology · 2026Article
- Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.Bayesian analysis · 2026Article
- Bayesian Geostatistics Using Predictive Stacking.Journal of the American Statistical Association · 2025Article
- Increasing certainty in systems biology models using Bayesian multimodel inference.Nature communications · 2025Article
- Stacking Models of Growth: A Methodology for Predicting the Pace of Progress to the Education Sustainable Development Targets Using International Large-Scale Assessments.Psychometrika · 2025Article
- A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data.BMC bioinformatics · 2024Article
- Article
- Local Bayesian Dirichlet mixing of imperfect models.Scientific reports · 2023Article
- Automating Model Comparison in Factor Graphs.Entropy (Basel, Switzerland) · 2023Article
- Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful.Bayesian analysis · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.