Likelihood Principle
The Likelihood Principle is a foundational tenet in statistical inference asserting that all evidence about a parameter contained in a sample is fully captured by the likelihood function, which specifies that any two sets of data yielding proportional likelihood functions provide identical information for inference about the parameter.
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37.6K
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342
Authors
205
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2017–2026
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University of Wisconsin–Madison
Madison, United States
6 h-index
7 publications
15.2K citations
Qingdao University of Science and Technology
Qingdao, China
5 h-index
12 publications
325 citations
University of California, Berkeley
Berkeley, United States
4 h-index
9 publications
1.1K citations
University of Michigan
Ann Arbor, United States
4 h-index
8 publications
5.5K citations
Harvard University
Cambridge, United States
4 h-index
8 publications
1.8K citations
Yale University
New Haven, United States
4 h-index
6 publications
2.6K citations
New York University
New York, United States
4 h-index
4 publications
735 citations
FH Aachen
Aachen, Germany
3 h-index
4 publications
334 citations
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19 publications · 17K citations
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