Mixture Density Estimation

Mixture Density Estimation is a density estimation method that models a probability density function as a weighted sum of parametric component densities, typically Gaussian distributions, to capture multimodal and heterogeneous data structures.

343

Publications

25.9K

Citations

719

Authors

373

Institutions

Publications per year

2017–2026

47

Authors

719

  1. UD
    Uwe D. Hanebeck

    Karlsruhe Institute of Technology, Germany

    7 publications · 118 citations

    7

    h-index

  2. KR
    Kathryn Roeder

    Carnegie Mellon University, United States

    5 publications · 782 citations

    5

    h-index

  3. NV
    Nikos Vlassis

    University of Amsterdam, Netherlands

    3 publications · 442 citations

    3

    h-index

  4. LW
    Larry Wasserman

    Carnegie Mellon University, United States

    3 publications · 708 citations

    3

    h-index

  5. CE
    Carey E. Priebe

    Johns Hopkins University, United States

    3 publications · 105 citations

    3

    h-index

1–5 of 719

Venues

  1. Journal of the American Statistical Association

    16 publications · 3.9K citations

    16

    h-index

  2. The Annals of Statistics

    12 publications · 1.6K citations

    12

    h-index

  3. arXiv (Cornell University)

    Repository · 13 publications · 415 citations

    12

    h-index

  4. Journal of Computational and Graphical Statistics

    JCGS · 9 publications · 357 citations

    9

    h-index

  5. 8

    h-index

1–5