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
288
1–5 of 288
850
10
h-index
8
h-index
7
h-index
5
h-index
4
h-index
1–5 of 850
383
UCLouvain
Louvain-la-Neuve, Belgium
11 h-index
30 publications
1.4K citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
35 publications
1.3K citations
University of Technology Sydney
Sydney, Australia
6 h-index
10 publications
396 citations

Beihang University
Beijing, China
6 h-index
18 publications
821 citations

Université Grenoble Alpes
Saint-Martin-d'Hères, France
5 h-index
13 publications
435 citations

Cornell University
Ithaca, United States
5 h-index
21 publications
537 citations
University College London
London, United Kingdom
5 h-index
12 publications
530 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
12 publications
497 citations
1–8 of 383
10 publications · 1.2K citations
10
h-index
Conference · 7 publications · 89 citations
7
h-index
Repository · 7 publications · 186 citations
7
h-index
6 publications · 337 citations
6
h-index
6 publications · 150 citations
6
h-index
1–5