Graphical Models

Graphical Models is a statistical framework that represents complex probabilistic relationships among variables through a graph structure, where nodes denote random variables and edges encode conditional dependencies, thereby distinguishing it from other statistical models by its explicit use of graph theory to factorize joint probability distributions.

4.2K

Publications

319.3K

Citations

8.7K

Authors

2.4K

Institutions

Publications per year

2017–2026

903

Authors

8.7K

  1. AS
    Alan S. Willsky

    Massachusetts Institute of Technology, United States

    32 publications · 2.8K citations

    20

    h-index

  2. SL
    Steffen L. Lauritzen

    Aalborg University, Denmark

    19 publications · 8.8K citations

    18

    h-index

  3. AM
    Andrew McCallum

    University of Massachusetts Amherst, United States

    21 publications · 15K citations

    17

    h-index

  4. RD
    Rina Dechter

    University of California, Irvine, United States

    29 publications · 1.1K citations

    17

    h-index

  5. AI
    Alexander Ihler

    University of California, Irvine, United States

    22 publications · 1.9K citations

    17

    h-index

1–5 of 8.7K

Venues

  1. arXiv (Cornell University)

    Repository · 313 publications · 17.8K citations

    55

    h-index

  2. Journal of the American Statistical Association

    62 publications · 11.7K citations

    42

    h-index

  3. The Annals of Statistics

    54 publications · 9.9K citations

    37

    h-index

  4. Bioinformatics

    50 publications · 6.9K citations

    29

    h-index

  5. Biometrika

    45 publications · 4.3K citations

    28

    h-index

1–5