Markov Random Fields

Markov Random Fields is a probabilistic graphical model that represents a set of random variables having a Markov property with respect to an undirected graph, wherein the conditional independence of a variable given all others is determined solely by its immediate neighbors.

2.3K

Publications

177.5K

Citations

5.1K

Authors

1.4K

Institutions

Publications per year

2017–2026

353

Variants

Markov Random FieldMrfUndirected Graphical ModelUndirected Graphical Models

Authors

5.1K

  1. AE
    Ayman El‐Baz

    University of Louisville, United States

    19 publications · 664 citations

    17

    h-index

  2. JZ
    Josiane Zerubia

    Institut national de recherche en sciences et technologies du numérique, France

    24 publications · 949 citations

    16

    h-index

  3. MJ
    Martin J. Wainwright

    University of California, Berkeley, United States

    17 publications · 2.2K citations

    15

    h-index

  4. RU
    Raquel Urtasun

    University of Toronto, Canada

    14 publications · 1.4K citations

    14

    h-index

  5. PB
    Patrick Bouthémy

    Institut de Recherche en Informatique et Systèmes Aléatoires, France

    13 publications · 622 citations

    12

    h-index

1–5 of 5.1K

Venues

  1. IEEE Transactions on Image Processing

    104 publications · 9K citations

    56

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 91 publications · 33.6K citations

    51

    h-index

  3. 42

    h-index

  4. IEEE Transactions on Medical Imaging

    57 publications · 7.9K citations

    38

    h-index

  5. arXiv (Cornell University)

    Repository · 51 publications · 3.7K citations

    24

    h-index

1–5