Probabilistic Interpolation

Probabilistic Interpolation is a spatial interpolation method that, as a member of the statistical interpolation class, fundamentally distinguishes itself by incorporating probabilistic models to quantify uncertainty in predicted values at unsampled locations.

30

Publications

1.1K

Citations

81

Authors

42

Institutions

Publications per year

2017–2026

12

Authors

81

  1. YW
    Yu Wang

    City University of Hong Kong, Hong Kong SAR China

    2 publications · 68 citations

    2

    h-index

  2. SC
    Sam Coveney

    University of Sheffield, United Kingdom

    1 publications · 29 citations

    1

    h-index

  3. GV
    Gonzalo Vegas‐Sánchez‐Ferrero

    Brigham and Women's Hospital, United States

    1 publications · 27 citations

    1

    h-index

  4. MK
    Mehdi Keblouti

    Badji Mokhtar-Annaba University, Algeria

    1 publications · 92 citations

    1

    h-index

  5. JE
    Jeremy E. Oakley

    University of Sheffield, United Kingdom

    1 publications · 29 citations

    1

    h-index

1–5 of 81

Venues

  1. Expert Systems with Applications

    1 publications · 14 citations

    1

    h-index

  2. Mathematical Geosciences

    1 publications · 34 citations

    1

    h-index

  3. IEEE Transactions on Emerging Topics in Computing

    ITETBT · 1 publications · 20 citations

    1

    h-index

  4. Energy

    1 publications · 13 citations

    1

    h-index

  5. Signal Processing Image Communication

    1 publications · 17 citations

    1

    h-index

1–5