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

Publications

27.1K

Citations

518

Authors

330

Institutions

Publications per year

2017–2026

23

Authors

518

  1. GJ
    Geoffrey J. McLachlan

    The University of Queensland, Australia

    11 publications · 396 citations

    11

    h-index

  2. GC
    Gilles Celeux

    Institut national de recherche en sciences et technologies du numérique, France

    8 publications · 2.8K citations

    8

    h-index

  3. TL
    Tsung‐I Lin

    National Chung Hsing University, Taiwan

    7 publications · 541 citations

    7

    h-index

  4. MW
    Michel Wedel

    University of Groningen, Netherlands

    7 publications · 551 citations

    7

    h-index

  5. JC
    Jiahua Chen

    University of British Columbia, Canada

    7 publications · 574 citations

    7

    h-index

1–5 of 518

Venues

  1. Journal of the American Statistical Association

    17 publications · 1.6K citations

    15

    h-index

  2. Biometrics

    14 publications · 1.5K citations

    14

    h-index

  3. Biometrika

    13 publications · 1.3K citations

    13

    h-index

  4. Communication in Statistics- Theory and Methods

    14 publications · 304 citations

    12

    h-index

  5. Statistics and Computing

    11 publications · 526 citations

    11

    h-index

1–5