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
Parents
65
Bayesian Computation and Stochastic Systems
Julian Besag, Peter Green, David Higdon et al. · Statistical Science · 1995 · 958 citations · Full text
On conditional and intrinsic autoregressions
Julian Besag, Charles Kooperberg · Biometrika · 1995 · 729 citations
Region Classification with Markov Field Aspect Models
Jakob Verbeek, Bill Triggs · 2007 · 190 citations · Full text
1–5 of 65
139
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Indian Institute of Science Education and Research Pune, India
2 publications · 83 citations
2
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2
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2
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1–5 of 139
103

Centre National de la Recherche Scientifique
Paris, France
4 h-index
7 publications
390 citations
Iowa State University
Ames, United States
3 h-index
6 publications
159 citations
University of California, Santa Barbara
Santa Barbara, United States
2 h-index
3 publications
487 citations
University of Connecticut
Storrs, United States
2 h-index
6 publications
120 citations
University of Washington
Seattle, United States
2 h-index
4 publications
1.6K citations
University of Washington Applied Physics Laboratory
Seattle, United States
2 h-index
4 publications
1.6K citations
Columbia University
New York, United States
2 h-index
4 publications
164 citations
West Virginia University
Morgantown, United States
2 h-index
2 publications
66 citations
1–8 of 103
3 publications · 186 citations
3
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2 publications · 66 citations
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JCGS · 2 publications · 66 citations
2
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AAP · 2 publications · 60 citations
2
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2 publications · 190 citations
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