Infinite Mixture Models

Infinite Mixture Models is a Bayesian nonparametric model that extends finite mixture models by allowing a countably infinite number of components, thereby enabling flexible and data-driven inference of cluster structure without pre-specifying the number of clusters.

241

Publications

13.5K

Citations

549

Authors

307

Institutions

Publications per year

2017–2026

58

Variants

Infinite Mixture Model

Authors

549

  1. NB
    Nizar Bouguila

    Concordia University, Canada

    17 publications · 592 citations

    15

    h-index

  2. ZG
    Zoubin Ghahramani

    University of Cambridge, United Kingdom

    9 publications · 1.2K citations

    9

    h-index

  3. AS
    Antonio Salmerón

    University of Almería, Spain

    5 publications · 208 citations

    5

    h-index

  4. CP
    Christian P. Robert

    Université Paris Dauphine-PSL, France

    5 publications · 126 citations

    5

    h-index

  5. JE
    Jim E. Griffin

    University of Kent, United Kingdom

    5 publications · 167 citations

    5

    h-index

1–5 of 549

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 533 citations

    11

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 8 publications · 369 citations

    8

    h-index

  3. The Annals of Statistics

    6 publications · 466 citations

    6

    h-index

  4. Statistics & Probability Letters

    6 publications · 124 citations

    6

    h-index

  5. 5

    h-index

1–5