Mixture Model

A Mixture Model is a probabilistic model that represents the presence of subpopulations within an overall population by combining multiple probability distributions, where each component distribution corresponds to a distinct subpopulation and the model assigns latent membership probabilities to observations.

6.4K

Publications

462.9K

Citations

16.1K

Authors

3.8K

Institutions

Publications per year

2017–2026

1.7K

Authors

16.1K

  1. NB
    Nizar Bouguila

    Concordia University, Canada

    63 publications · 2.7K citations

    29

    h-index

  2. GJ
    Geoffrey J. McLachlan

    The University of Queensland, Australia

    47 publications · 4.1K citations

    27

    h-index

  3. JA
    John A. Cornell

    University of Florida, United States

    26 publications · 2.6K citations

    21

    h-index

  4. PD
    Paul D. McNicholas

    University of Guelph, Canada

    22 publications · 1.8K citations

    18

    h-index

  5. BG
    Bruce G. Lindsay

    Pennsylvania State University, United States

    20 publications · 5.3K citations

    18

    h-index

1–5 of 16.1K

Venues

  1. Journal of the American Statistical Association

    107 publications · 22.9K citations

    51

    h-index

  2. Biometrics

    97 publications · 9.5K citations

    41

    h-index

  3. Bioinformatics

    71 publications · 5.6K citations

    39

    h-index

  4. 37

    h-index

  5. arXiv (Cornell University)

    Repository · 128 publications · 5.8K citations

    37

    h-index

1–5