mlsanityData to 7 Oct 2026

Bayesian data fusion with shared priors

arXiv 2212.07311 ↗First posted 14 Dec 2022cs.LG stat.MLcited by 3

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Abstract (arXiv)

The integration of data and knowledge from several sources is known as data fusion. When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential. In Bayesian settings, a priori information of the unknown quantities is available and, possibly, present among the different distributed estimators. When the local estimates are fused, the prior knowledge used to construct several local posteriors might be overused unless the fusion node accounts for this and corrects it. In this paper, we analyze the effects of shared priors in Bayesian data fusion contexts. Depending on different common fusion rules, our analysis helps to understand the performance behavior as a function of the number of collaborative agents and as a consequence of different types of priors. The analysis is performed by using two divergences which are common in Bayesian inference, and the generality of the results allows to analyze very generic distributions. These theoretical results are corroborated through experiments in a variety of estimation and classification problems, including linear and nonlinear models, and federated learning schemes.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 14 Dec 2022.

Builds on

17 of 58 references.

References · 17

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5 more
  1. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  2. 2022Fusion of Probability Density Functionscited by 22
  3. 2016A Universal Approximation Theorem for Mixture of Experts Modelscited by 17
  4. 2021Personalized Federated Learning over non-IID Data for Indoor Localizationcited by 6
  5. 2019Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusioncited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Model Merging by Uncertainty-Based Gradient Matchingcited by 77
  2. 2025Knowledge Adaptation as Posterior Correctioncited by 6
  3. 2024A Bayesian Framework for Clustered Federated Learningcited by 3