Bayesian Network Structure Learning

Bayesian Network Structure Learning is a computational method in probabilistic graphical modeling that identifies the directed acyclic graph structure representing conditional dependencies among random variables from observational data.

764

Publications

57.3K

Citations

1.6K

Authors

648

Institutions

Publications per year

2017–2026

195

Variants

Bayesian Network Structure Discovery

Authors

1.6K

  1. NF
    Nir Friedman

    Hebrew University of Jerusalem, Israel

    18 publications · 2.6K citations

    17

    h-index

  2. PL
    Pedro Larrañaga

    Universidad Politécnica de Madrid, Spain

    17 publications · 1.3K citations

    16

    h-index

  3. MS
    Marco Scutari

    Dalle Molle Institute for Artificial Intelligence Research, Switzerland

    11 publications · 2.2K citations

    11

    h-index

  4. BM
    Brandon Malone

    Helsinki Institute for Information Technology, Finland

    11 publications · 423 citations

    11

    h-index

  5. CP
    Cassio P. de Campos

    Dalle Molle Institute for Artificial Intelligence Research, Switzerland

    10 publications · 650 citations

    10

    h-index

1–5 of 1.6K

Venues

  1. arXiv (Cornell University)

    Repository · 76 publications · 5.1K citations

    32

    h-index

  2. International Journal of Approximate Reasoning

    31 publications · 1.8K citations

    23

    h-index

  3. Machine Learning

    12 publications · 13.4K citations

    12

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 11 publications · 296 citations

    11

    h-index

  5. Expert Systems with Applications

    12 publications · 426 citations

    11

    h-index

1–5