Gaussian Process Smoothing

Gaussian Process Smoothing is a spatial smoothing method that employs a Gaussian process as a probabilistic model to estimate underlying continuous fields by assigning prior distributions over functions and updating them through observed data.

115

Publications

5.6K

Citations

341

Authors

190

Institutions

Publications per year

2017–2026

43

Authors

341

  1. ZJ
    Zhewei Jiang

    Columbia University, United States

    4 publications · 494 citations

    4

    h-index

  2. YC
    Yao‐Wen Chang

    National Taiwan University, Taiwan

    4 publications · 494 citations

    4

    h-index

  3. TC
    Tung-Chieh Chen

    National Taiwan University, Taiwan

    3 publications · 447 citations

    3

    h-index

  4. MS
    Marina Seikel

    University of Cape Town, South Africa

    3 publications · 73 citations

    3

    h-index

  5. EG
    Elad Gilboa

    Washington University in St. Louis, United States

    3 publications · 119 citations

    3

    h-index

1–5 of 341

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 194 citations

    7

    h-index

  2. The Astrophysical Journal

    ApJ · 5 publications · 265 citations

    4

    h-index

  3. 3

    h-index

  4. Energy Procedia

    2 publications · 52 citations

    2

    h-index

  5. NeuroImage

    2 publications · 106 citations

    2

    h-index

1–5