Bayesian Data Fusion

Bayesian Data Fusion is a Bayesian inference framework that integrates heterogeneous, multi-source data by probabilistically modeling their joint distributions under uncertainty, distinguishing itself through the explicit use of prior knowledge and Bayes' theorem to update beliefs about shared latent variables across domains.

288

Publications

12.2K

Citations

850

Authors

383

Institutions

Publications per year

2017–2026

93

Authors

850

  1. PB
    Patrick Bogaert

    UCLouvain, Belgium

    10 publications · 488 citations

    10

    h-index

  2. DF
    Dominique Fasbender

    UCLouvain, Belgium

    8 publications · 503 citations

    8

    h-index

  3. XL
    Xin Li

    Carnegie Mellon University, United States

    7 publications · 189 citations

    7

    h-index

  4. MC
    Mark Campbell

    Cornell University, United States

    5 publications · 128 citations

    5

    h-index

  5. FW
    Fa Wang

    Carnegie Mellon University, United States

    4 publications · 135 citations

    4

    h-index

1–5 of 850

Venues

  1. Information Fusion

    10 publications · 1.2K citations

    10

    h-index

  2. International Conference on Information Fusion

    Conference · 7 publications · 89 citations

    7

    h-index

  3. arXiv (Cornell University)

    Repository · 7 publications · 186 citations

    7

    h-index

  4. IEEE Transactions on Signal Processing

    6 publications · 337 citations

    6

    h-index

  5. 6

    h-index

1–5