Likelihood For Mixture Models
Likelihood for Mixture Models is a statistical framework that specifies the probability of observed data under a parametric model composed of multiple subpopulations, distinguishing itself by incorporating latent component assignments and mixing proportions to account for heterogeneity in the data-generating process.
317
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27.1K
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518
Authors
330
Institutions
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2017–2026
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Estimating the components of a mixture of normal distributions
N. E. Day · Biometrika · 1969 · 824 citations
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Institut national de recherche en sciences et technologies du numérique, France
8 publications · 2.8K citations
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The University of Queensland
Brisbane, Australia
11 h-index
19 publications
536 citations

Centre National de la Recherche Scientifique
Paris, France
10 h-index
21 publications
3.8K citations

Pennsylvania State University
State College, United States
9 h-index
13 publications
750 citations
University of Washington
Seattle, United States
8 h-index
11 publications
1.1K citations
University of Michigan
Ann Arbor, United States
6 h-index
7 publications
579 citations

National Chung Hsing University
Taichung, Taiwan
6 h-index
7 publications
360 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
5 h-index
6 publications
154 citations
Université de Technologie de Compiègne
Compiègne, France
5 h-index
5 publications
2.3K citations
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17 publications · 1.6K citations
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