Markov Network Models

Markov Network Models is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via an undirected graph, distinguished from other graphical models by its use of undirected edges to encode symmetric dependence relations and its foundation in the Markov property for characterizing local independence.

309

Publications

23.2K

Citations

756

Authors

362

Institutions

Publications per year

2017–2026

61

Variants

MrfUndirected Graphical Models

Authors

756

  1. PD
    Pedro Domingos

    University of Washington, United States

    8 publications · 514 citations

    8

    h-index

  2. BT
    Ben Taskar

    University of Pennsylvania, United States

    6 publications · 1.5K citations

    6

    h-index

  3. DK
    Daphne Koller

    Stanford University, United States

    6 publications · 1.4K citations

    6

    h-index

  4. JD
    Jesse Davis

    KU Leuven, Belgium

    5 publications · 246 citations

    5

    h-index

  5. JM
    John McCall

    Robert Gordon University, United Kingdom

    5 publications · 120 citations

    5

    h-index

1–5 of 756

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 1K citations

    15

    h-index

  2. 7

    h-index

  3. Performance Evaluation

    3 publications · 205 citations

    3

    h-index

  4. Lirias (KU Leuven)

    Repository · 3 publications · 103 citations

    3

    h-index

  5. Scientific Reports

    3 publications · 161 citations

    3

    h-index

1–5