Markov Random Variables

Markov Random Variables is a probabilistic graphical model that represents a set of random variables with a Markov property, wherein each variable is conditionally independent of non-adjacent variables given its neighbors in an undirected graph structure.

586

Publications

50.9K

Citations

1.1K

Authors

502

Institutions

Publications per year

2017–2026

43

Authors

1.1K

  1. JZ
    Josiane Zerubia

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

    6 publications · 349 citations

    6

    h-index

  2. KT
    Kazuyuki Tanaka

    Tohoku University, Japan

    5 publications · 91 citations

    5

    h-index

  3. SI
    Sidney I. Resnick

    Cornell University, United States

    5 publications · 175 citations

    5

    h-index

  4. JL
    John Lafferty

    Carnegie Mellon University, United States

    5 publications · 361 citations

    5

    h-index

  5. MY
    Muneki Yasuda

    Tohoku University, Japan

    5 publications · 96 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. The Annals of Probability

    20 publications · 1.2K citations

    16

    h-index

  2. Journal of Applied Probability

    JAP · 29 publications · 628 citations

    16

    h-index

  3. IEEE Transactions on Image Processing

    16 publications · 1.8K citations

    15

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 13 publications · 1.2K citations

    13

    h-index

  5. Advances in Applied Probability

    AAP · 14 publications · 424 citations

    12

    h-index

1–5