Spatial Markov Random Fields

Spatial Markov Random Fields is a probabilistic graphical model that represents spatially correlated random variables through an undirected graph structure, where the probability distribution of each variable depends only on its neighbors, embodying the Markov property to capture local spatial dependencies in georeferenced data.

65

Publications

4.4K

Citations

139

Authors

103

Institutions

Publications per year

2017–2026

8

Variants

Spatial Mrfs

Authors

139

  1. DH
    David Higdon

    Los Alamos National Laboratory, United States

    3 publications · 989 citations

    3

    h-index

  2. JB
    Julian Besag

    University of Washington, United States

    3 publications · 1.7K citations

    3

    h-index

  3. DM
    Debashis Mondal

    Indian Institute of Science Education and Research Pune, India

    2 publications · 83 citations

    2

    h-index

  4. AD
    Akhil Datta‐Gupta

    Texas A&M University, United States

    2 publications · 31 citations

    2

    h-index

  5. TL
    Tanguy Le Borgne

    Géosciences Rennes, France

    2 publications · 154 citations

    2

    h-index

1–5 of 139

Venues

  1. Advances in Water Resources

    3 publications · 186 citations

    3

    h-index

  2. Papers of the Regional Science Association

    2 publications · 66 citations

    2

    h-index

  3. Journal of Computational and Graphical Statistics

    JCGS · 2 publications · 66 citations

    2

    h-index

  4. Advances in Applied Probability

    AAP · 2 publications · 60 citations

    2

    h-index

  5. Spatial Statistics

    2 publications · 190 citations

    2

    h-index

1–5