Random Field Estimation

Random Field Estimation is a statistical inference method that characterizes the spatial distribution of a stochastic process by predicting the values of a random field at unobserved locations based on observed data and an assumed covariance structure.

187

Publications

8.3K

Citations

345

Authors

229

Institutions

Publications per year

2017–2026

31

Authors

345

  1. ML
    Michael L. Stein

    University of Chicago, United States

    4 publications · 264 citations

    4

    h-index

  2. YW
    Yu Wang

    City University of Hong Kong, Hong Kong SAR China

    4 publications · 333 citations

    4

    h-index

  3. LT
    Lanh Tat Tran

    Indiana University Bloomington, United States

    4 publications · 367 citations

    4

    h-index

  4. PH
    Philip H. S. Torr

    Oxford Brookes University, United Kingdom

    4 publications · 162 citations

    4

    h-index

  5. JM
    Joseph M. Francos

    Ben-Gurion University of the Negev, Israel

    4 publications · 76 citations

    4

    h-index

1–5 of 345

Venues

  1. The Annals of Statistics

    10 publications · 651 citations

    10

    h-index

  2. IEEE Transactions on Signal Processing

    10 publications · 382 citations

    10

    h-index

  3. IEEE Transactions on Information Theory

    7 publications · 200 citations

    7

    h-index

  4. Journal of Multivariate Analysis

    6 publications · 332 citations

    6

    h-index

  5. 6

    h-index

1–5