Belief Networks

Belief Networks is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph, encoding uncertain knowledge through probability distributions to support reasoning under uncertainty.

1.5K

Publications

132.4K

Citations

3.4K

Authors

1.2K

Institutions

Publications per year

2017–2026

346

Authors

3.4K

  1. GF
    Gregory F. Cooper

    University of Pittsburgh, United States

    17 publications · 2.8K citations

    14

    h-index

  2. RD
    Rina Dechter

    University of California, Irvine, United States

    14 publications · 1.1K citations

    12

    h-index

  3. DH
    David Heckerman

    Microsoft (United States), United States

    13 publications · 1.9K citations

    12

    h-index

  4. LC
    Linda C. van der Gaag

    Utrecht University, Netherlands

    14 publications · 870 citations

    12

    h-index

  5. RR
    Ronald R. Yager

    Iona College, United States

    14 publications · 450 citations

    12

    h-index

1–5 of 3.4K

Venues

  1. arXiv (Cornell University)

    Repository · 121 publications · 9.5K citations

    43

    h-index

  2. International Journal of Approximate Reasoning

    73 publications · 3.9K citations

    32

    h-index

  3. Artificial Intelligence

    31 publications · 10.9K citations

    24

    h-index

  4. Information Sciences

    14 publications · 523 citations

    13

    h-index

  5. Expert Systems with Applications

    12 publications · 1.5K citations

    12

    h-index

1–5