Bayesian Finite Mixture Models

Bayesian Finite Mixture Model is a probabilistic clustering framework that specifies a finite number of parametric component distributions, combined under a Bayesian paradigm to jointly infer latent cluster assignments and model parameters through posterior distributions.

230

Publications

14.8K

Citations

462

Authors

312

Institutions

Publications per year

2017–2026

44

Authors

462

  1. NB
    Nizar Bouguila

    Concordia University, Canada

    13 publications · 589 citations

    12

    h-index

  2. KM
    Kerrie Mengersen

    Queensland University of Technology, Australia

    6 publications · 179 citations

    6

    h-index

  3. SF
    Sylvia Frühwirth‐Schnatter

    Vienna University of Economics and Business, Austria

    6 publications · 130 citations

    6

    h-index

  4. EK
    Essam K. AL-Hussaini

    Assiut University, Egypt

    5 publications · 112 citations

    5

    h-index

  5. AN
    Agostino Nobile

    University of Glasgow, United Kingdom

    4 publications · 225 citations

    4

    h-index

1–5 of 462

Venues

  1. Computational Statistics & Data Analysis

    CSDA · 13 publications · 483 citations

    12

    h-index

  2. Statistics and Computing

    9 publications · 563 citations

    9

    h-index

  3. Journal of Statistical Planning and Inference

    6 publications · 215 citations

    6

    h-index

  4. The Annals of Statistics

    6 publications · 811 citations

    6

    h-index

  5. 6

    h-index

1–5