Likelihood-based Computation
Likelihood-Based Computation is a computational methodology within statistical inference that specifically involves the use of likelihood functions—the probability of observed data given model parameters—to derive or estimate parameters, perform hypothesis testing, or conduct model selection through systematic algorithmic procedures.
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31.1K
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5.7K
Authors
632
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2017–2026
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ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE
Darryl I. MacKenzie, James D. Nichols, Gideon B. Lachman et al. · Ecology · 2002 · 4.4K citations
<scp>UCSF ChimeraX</scp>: Tools for structure building and analysis
Elaine C. Meng, Thomas D. Goddard, Eric F. Pettersen et al. · Protein Science · 2023 · 4.2K citations · Full text
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National Taiwan University of Science and Technology, Taiwan
4 publications · 243 citations
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University of Michigan
Ann Arbor, United States
11 h-index
82 publications
15.8K citations
University of Cambridge
Cambridge, United Kingdom
11 h-index
63 publications
10.5K citations

Centre National de la Recherche Scientifique
Paris, France
11 h-index
372 publications
78K citations
University of California, Berkeley
Berkeley, United States
10 h-index
83 publications
16K citations
University of Washington
Seattle, United States
9 h-index
38 publications
6.8K citations

University of Waterloo
Waterloo, Canada
8 h-index
12 publications
1.6K citations

University of Toronto
Toronto, Canada
7 h-index
57 publications
12.9K citations
CEA Paris-Saclay
Gif-sur-Yvette, France
6 h-index
94 publications
17.2K citations
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9 publications · 568 citations
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Repository · 9 publications · 2.3K citations
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MBE · 7 publications · 2.4K citations
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5 publications · 371 citations
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5 publications · 1.1K citations
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