Finite Mixture Models

Finite Mixture Model is a probabilistic model that represents a population as a finite combination of distinct component distributions, each corresponding to a subpopulation, where the overall distribution is a weighted sum of these components with parameters governing mixture proportions and component-specific characteristics.

2.4K

Publications

261.5K

Citations

4.6K

Authors

1.8K

Institutions

Publications per year

2017–2026

464

Variants

Finite Mixture Modeling

Authors

4.6K

  1. NB
    Nizar Bouguila

    Concordia University, Canada

    62 publications · 2.8K citations

    31

    h-index

  2. GJ
    Geoffrey J. McLachlan

    The University of Queensland, Australia

    30 publications · 3.2K citations

    22

    h-index

  3. BG
    Bruce G. Lindsay

    Pennsylvania State University, United States

    18 publications · 6.1K citations

    17

    h-index

  4. DZ
    Djemel Ziou

    Université de Sherbrooke, Canada

    20 publications · 1.3K citations

    17

    h-index

  5. JC
    Jiahua Chen

    University of British Columbia, Canada

    20 publications · 1.5K citations

    16

    h-index

1–5 of 4.6K

Venues

  1. Journal of the American Statistical Association

    66 publications · 14.7K citations

    45

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 67 publications · 3.8K citations

    37

    h-index

  3. 30

    h-index

  4. The Annals of Statistics

    35 publications · 4.5K citations

    27

    h-index

  5. Statistics and Computing

    43 publications · 3.2K citations

    27

    h-index

1–5