Bayesian Inference For Random Fields
Bayesian Inference for Random Fields is a statistical framework that employs Bayesian principles to estimate and update the probabilistic structure of random fields by integrating observed data with prior knowledge, distinguishing itself through the explicit modeling of spatial or temporal dependencies within a hierarchical Bayesian setting.
91
Publications
6.3K
Citations
184
Authors
118
Institutions
Publications per year
2017–2026
17
Parents
91
A Bayesian Analysis of Kriging
Mark S. Handcock, Michael L. Stein · Technometrics · 1993 · 560 citations
Practical statistics for field biology
Soumeya Fodili-In Albon · Trends in Ecology & Evolution · 1991 · 323 citations
1–5 of 91
184
The University of Texas at San Antonio, United States
4 publications · 412 citations
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3
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3
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3
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2
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1–5 of 184
118

City University of Hong Kong
Hong Kong, Hong Kong
5 h-index
9 publications
1K citations
Norwegian University of Science and Technology
Trondheim, Norway
4 h-index
4 publications
550 citations
University of Chicago
Chicago, United States
3 h-index
3 publications
745 citations
Technical University of Munich
Munich, Germany
3 h-index
8 publications
290 citations
National University of Singapore
Singapore, Singapore
3 h-index
3 publications
222 citations

Universitat Jaume I
Castellon, Spain
2 h-index
4 publications
116 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
4 publications
92 citations
University of Oxford
Oxford, United Kingdom
2 h-index
2 publications
144 citations
1–8 of 118
4 publications · 333 citations
4
h-index
3 publications · 145 citations
3
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JCGS · 3 publications · 115 citations
3
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MSSP · 2 publications · 35 citations
2
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2 publications · 234 citations
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1–5