Random Field Inference is a statistical methodology that belongs to the class of spatial inference techniques, specifically characterized by its use of probabilistic models to draw conclusions about underlying spatial processes from observed data distributed over continuous domains.
52
Publications
3.8K
Citations
119
Authors
90
Institutions
Publications per year
2017–2026
9
Parents
Publications
52
Nonstationary cluster-size inference with random field and permutation methods
Satoru Hayasaka, K. Luan Phan, Israel Liberzon et al. · NeuroImage · 2004 · 671 citations
Selective Sampling for Nearest Neighbor Classifiers
Michael Lindenbaum, Shaul Markovitch, Dmitry Rusakov · Machine Learning · 2004 · 188 citations · Full text
1–5 of 52
Authors
119
- TE
5
h-index
- KJ
3
h-index
- KF
3
h-index
- MK
2
h-index
- GF
2
h-index
1–5 of 119
Institutions
90
University of Michigan
Ann Arbor, United States
4 h-index
8 publications
2.5K citations
University of Oxford
Oxford, United Kingdom
3 h-index
3 publications
238 citations
New York University
New York, United States
3 h-index
3 publications
465 citations
Max Planck Institute for Informatics
Saarbrücken, Germany
2 h-index
3 publications
277 citations
University of Warwick
Coventry, United Kingdom
2 h-index
4 publications
312 citations
University of Southern California
Los Angeles, United States
2 h-index
4 publications
508 citations
Sun Yat-sen University
Guangzhou, China
2 h-index
3 publications
96 citations
Technion – Israel Institute of Technology
Haifa, Israel
2 h-index
6 publications
633 citations
1–8 of 90
Venues
- NeuroImage
5 publications · 1.1K citations
5
h-index
- Journal of the American Statistical Association
4 publications · 501 citations
4
h-index
- Journal of Statistical Software
JSS · 3 publications · 320 citations
3
h-index
- Human Brain Mapping
3 publications · 94 citations
3
h-index
- Mathematical Geosciences
2 publications · 55 citations
2
h-index
1–5