Random Field Approximation
Random Field Approximation is a mathematical framework that belongs to the class of random field modeling techniques, distinguished by its purpose of constructing simplified stochastic representations to approximate complex spatially or temporally correlated random phenomena while preserving essential statistical and structural properties.
364
Publications
21.3K
Citations
710
Authors
411
Institutions
Publications per year
2017–2026
82
Parents
364
Nonstationary cluster-size inference with random field and permutation methods
Satoru Hayasaka, K. Luan Phan, Israel Liberzon et al. · NeuroImage · 2004 · 671 citations
1–5 of 364
710
University of Illinois Urbana-Champaign, United States
10 publications · 570 citations
10
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7
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6
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6
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5
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1–5 of 710
411

Centre National de la Recherche Scientifique
Paris, France
20 h-index
47 publications
2.1K citations
University of Illinois Urbana-Champaign
Urbana, United States
10 h-index
19 publications
493 citations
Columbia University
New York, United States
8 h-index
11 publications
967 citations
ETH Zurich
Zurich, Switzerland
8 h-index
11 publications
1.2K citations
Laboratoire Modélisation et Simulation Multi-Echelle
Champs-sur-Marne, France
7 h-index
12 publications
454 citations

Cornell University
Ithaca, United States
7 h-index
12 publications
643 citations

UNSW Sydney
Sydney, Australia
7 h-index
19 publications
416 citations
Stanford University
Stanford, United States
6 h-index
11 publications
366 citations
1–8 of 411
PRB · 14 publications · 1.8K citations
12
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12 publications · 765 citations
12
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9 publications · 385 citations
9
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8 publications · 227 citations
8
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PRL · 8 publications · 577 citations
8
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