Bayesian Graphical Models

Bayesian Graphical Models is a probabilistic graphical model that represents conditional dependencies among random variables through a directed acyclic graph, where the joint probability distribution is specified using Bayesian inference principles to encode uncertainty and facilitate coherent updating of beliefs.

1.4K

Publications

123.3K

Citations

2.7K

Authors

1K

Institutions

Publications per year

2017–2026

262

Authors

2.7K

  1. DH
    David Heckerman

    Microsoft (United States), United States

    21 publications · 7.8K citations

    18

    h-index

  2. DK
    Daphne Koller

    Stanford University, United States

    16 publications · 2.6K citations

    16

    h-index

  3. AD
    Adnan Darwiche

    University of California, Los Angeles, United States

    20 publications · 855 citations

    15

    h-index

  4. NF
    Nir Friedman

    Hebrew University of Jerusalem, Israel

    13 publications · 1.7K citations

    13

    h-index

  5. DG
    Dan Geiger

    Technion – Israel Institute of Technology, Israel

    13 publications · 6.5K citations

    12

    h-index

1–5 of 2.7K

Venues

  1. arXiv (Cornell University)

    Repository · 147 publications · 12.3K citations

    42

    h-index

  2. International Journal of Approximate Reasoning

    62 publications · 2.6K citations

    29

    h-index

  3. Journal of the American Statistical Association

    19 publications · 6.8K citations

    18

    h-index

  4. Artificial Intelligence

    19 publications · 4.6K citations

    16

    h-index

  5. Reliability Engineering & System Safety

    15 publications · 1.7K citations

    14

    h-index

1–5