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Bayesian model averaging: A systematic review and conceptual classification

arXiv 1509.08864 ↗First posted 29 Sep 2015stat.ME stat.APcited by 29

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Abstract (arXiv)

Bayesian Model Averaging (BMA) is an application of Bayesian inference to the problems of model selection, combined estimation and prediction that produces a straightforward model choice criteria and less risky predictions. However, the application of BMA is not always straightforward, leading to diverse assumptions and situational choices on its different aspects. Despite the widespread application of BMA in the literature, there were not many accounts of these differences and trends besides a few landmark revisions in the late 1990s and early 2000s, therefore not taking into account any advancements made in the last 15 years. In this work, we present an account of these developments through a careful content analysis of 587 articles in BMA published between 1996 and 2014. We also develop a conceptual classification scheme to better describe this vast literature, understand its trends and future directions and provide guidance for the researcher interested in both the application and development of the methodology. The results of the classification scheme and content review are then used to discuss the present and future of the BMA literature.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Sep 2015.

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Citing papers per year: 2021: 5; 2022: 8; 2023: 6; 2024: 4; 2025: 1; 2026: 5.

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  1. 2022Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Modelscited by 191
  2. 2021Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimationcited by 71
  3. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
  4. 2022A review of predictive uncertainty estimation with machine learningcited by 29
  5. 2021CRPS Learningcited by 21
  6. 2022Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworkscited by 13
  7. 2022ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Trainingcited by 7
  8. 2023Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Reviewcited by 7
  9. 2023Collapsed Inference for Bayesian Deep Learningcited by 6
  10. 2022clusterBMA: Bayesian model averaging for clusteringcited by 2
  11. 2023Local Bayesian Dirichlet mixing of imperfect modelscited by 2
  12. 2024Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passingcited by 2
17 more
  1. 2021WiSleep: Inferring Sleep Duration at Scale Using Passive WiFi Sensingcited by 1
  2. 2022Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interfacecited by 1
  3. 2022A model aggregation approach for high-dimensional large-scale optimizationcited by 1
  4. 2023Stability and L2-penalty in Model Averagingcited by 1
  5. 2024Model orthogonalization and Bayesian forecast mixing via Principal Component Analysiscited by 1
  6. 2024Flat Posterior Does Matter For Bayesian Model Averagingcited by 1
  7. 2024Network Fission Ensembles for Low-Cost Self-Ensemblescited by 1
  8. 2026ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Modelscited by 1
  9. 2026A formal definition and meta-model for a machine theory of mindcited by 1
  10. 2021Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Knowncited by 0
  11. 2022Feature Selection using e-valuescited by 0
  12. 2023Sparse Mutation Decompositions: Fine Tuning Deep Neural Networks with Subspace Evolutioncited by 0
  13. 2023Automating Model Comparison in Factor Graphscited by 0
  14. 2025Achieving Skilled and Reliable Daily Probabilistic Forecasts of Wind Power at Subseasonal-to-Seasonal Timescales over Francecited by 0
  15. 2026PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregationcited by 0
  16. 2026Uncertainty-Aware Sparse Identification of Dynamical Systems via Bayesian Model Averagingcited by 0
  17. 2026An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanismcited by 0