Maximum Entropy Modeling
Maximum Entropy Modeling is a statistical modeling framework within habitat suitability analysis that belongs to the class of presence-only species distribution models and is distinguished by its use of the principle of maximum entropy to estimate the most uniform probability distribution of species occurrences given environmental constraints and observed presence data.
2.1K
Publications
165.9K
Citations
6.8K
Authors
2.3K
Institutions
Publications per year
2017–2026
776
Variants
Maxent Modeling
Parents
2.1K
A statistical explanation of MaxEnt for ecologists
Jane Elith, Steven J. Phillips, Trevor Hastie et al. · Diversity and Distributions · 2010 · 6.6K citations · Full text
Richard G. Pearson, Christopher J. Raxworthy, Miguel Nakamura et al. · Journal of Biogeography · 2006 · 3.1K citations · Full text
Life Sciences, Ecology, Zoology +13
1–5 of 2.1K
6.8K
University of North Carolina at Chapel Hill, United States
12 publications · 546 citations
11
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9
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Lappeenranta-Lahti University of Technology, Finland
9 publications · 606 citations
9
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9
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9
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1–5 of 6.8K
2.3K

Chinese Academy of Sciences
Beijing, China
33 h-index
322 publications
11.8K citations
University of Chinese Academy of Sciences
Beijing, China
24 h-index
128 publications
4.9K citations
University of California, Berkeley
Berkeley, United States
17 h-index
33 publications
2.3K citations
University of Oxford
Oxford, United Kingdom
16 h-index
41 publications
6.8K citations
United States Geological Survey
Reston, United States
16 h-index
31 publications
2.6K citations
Carnegie Mellon University
Pittsburgh, United States
15 h-index
28 publications
23.6K citations
Princeton University
Princeton, United States
14 h-index
23 publications
22.6K citations
University of Pennsylvania
Philadelphia, United States
14 h-index
35 publications
2.7K citations
1–8 of 2.3K
76 publications · 4.9K citations
43
h-index
40 publications · 1.5K citations
25
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35 publications · 1.6K citations
21
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23 publications · 12.4K citations
21
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27 publications · 1.4K citations
21
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