Fast Kernel Density Estimation

Fast Kernel Density Estimation is a computational method in nonparametric statistics that accelerates the evaluation of kernel density estimates by employing approximation techniques or algorithmic optimizations to reduce time complexity relative to exact kernel summation.

62

Publications

3.4K

Citations

142

Authors

94

Institutions

Publications per year

2017–2026

20

Variants

Fast Kde

Authors

142

  1. AG
    Alexander Gray

    Georgia Institute of Technology, United States

    4 publications · 101 citations

    4

    h-index

  2. RD
    Ramani Duraiswami

    University of Maryland, College Park, United States

    3 publications · 466 citations

    3

    h-index

  3. DL
    Dongryeol Lee

    Georgia Institute of Technology, United States

    3 publications · 81 citations

    3

    h-index

  4. BS
    Burkhardt Seifert

    University of Zurich, Switzerland

    2 publications · 71 citations

    2

    h-index

  5. JE
    Joachim Engel

    Ludwigsburg University of Education, Germany

    2 publications · 71 citations

    2

    h-index

1–5 of 142

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 5 publications · 232 citations

    5

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 4 publications · 249 citations

    4

    h-index

  3. IEEE Transactions on Image Processing

    2 publications · 91 citations

    2

    h-index

  4. 2

    h-index

  5. Measurement Techniques

    2 publications · 24 citations

    2

    h-index

1–5