Information-theoretic Model Selection
Information-Theoretic Model Selection is a model selection paradigm within evolutionary model selection that uses information-theoretic criteria, such as Akaike's Information Criterion or Bayesian Information Criterion, to balance model fit and complexity by minimizing the information loss between the model and the observed data.
186
Publications
80.3K
Citations
439
Authors
272
Institutions
Publications per year
2017–2026
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United States Geological Survey
Reston, United States
5 h-index
9 publications
194 citations
Colorado State University
Fort Collins, United States
5 h-index
10 publications
14K citations
University of Florida
Gainesville, United States
5 h-index
7 publications
198 citations
University of California, Berkeley
Berkeley, United States
4 h-index
6 publications
289 citations

Aarhus University
Aarhus, Denmark
4 h-index
9 publications
794 citations
University of Wisconsin–Madison
Madison, United States
4 h-index
11 publications
320 citations
University of Iowa
Iowa City, United States
3 h-index
3 publications
224 citations

The Nature Conservancy
Arlington, United States
3 h-index
4 publications
76 citations
1–8 of 272
7 publications · 45.1K citations
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5 publications · 916 citations
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5 publications · 762 citations
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CSDA · 5 publications · 335 citations
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4 publications · 309 citations
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