Bayesian Spatial Prediction

Bayesian Spatial Prediction is a statistical framework for spatial inference that formally quantifies uncertainty in predicted values at unobserved locations by combining prior distributions over spatial processes with observed data through Bayes' theorem.

139

Publications

7.5K

Citations

428

Authors

261

Institutions

Publications per year

2017–2026

36

Authors

428

  1. AC
    Archie C. A. Clements

    Australian National University, Australia

    5 publications · 433 citations

    5

    h-index

  2. AE
    Alan E. Gelfand

    Duke University, United States

    5 publications · 835 citations

    5

    h-index

  3. SB
    Sudipto Banerjee

    University of Minnesota, United States

    4 publications · 184 citations

    4

    h-index

  4. JV
    James V. Zidek

    University of British Columbia, Canada

    4 publications · 112 citations

    4

    h-index

  5. JC
    Jongeun Choi

    Michigan State University, United States

    3 publications · 94 citations

    3

    h-index

1–5 of 428

Venues

  1. 6

    h-index

  2. Environmetrics

    6 publications · 310 citations

    6

    h-index

  3. Journal of Computational and Graphical Statistics

    JCGS · 4 publications · 123 citations

    4

    h-index

  4. Biometrics

    4 publications · 471 citations

    4

    h-index

  5. 3

    h-index

1–5