Bayesian Spatial Smoothing

Bayesian Spatial Smoothing is a statistical modeling framework that incorporates prior distributions over spatially correlated parameters to estimate smoothed spatial patterns, distinguishing itself from classical spatial smoothing methods by formally accounting for uncertainty through Bayesian inference.

47

Publications

2.1K

Citations

118

Authors

75

Institutions

Publications per year

2017–2026

10

Authors

118

  1. BP
    Bradley P. Carlin

    University of Minnesota, United States

    4 publications · 195 citations

    4

    h-index

  2. KM
    Kerrie Mengersen

    Queensland University of Technology, Australia

    4 publications · 137 citations

    4

    h-index

  3. ED
    Earl Duncan

    Queensland University of Technology, Australia

    3 publications · 121 citations

    3

    h-index

  4. JH
    Jeffrey H. Dorfman

    University of Georgia, United States

    2 publications · 61 citations

    2

    h-index

  5. CJ
    Christopher J. Paciorek

    University of California, Berkeley, United States

    2 publications · 556 citations

    2

    h-index

1–5 of 118

Venues

  1. Statistics in Medicine

    4 publications · 94 citations

    4

    h-index

  2. Environmetrics

    3 publications · 518 citations

    3

    h-index

  3. 2

    h-index

  4. Journal of Computational and Graphical Statistics

    JCGS · 2 publications · 77 citations

    2

    h-index

  5. International Journal of Health Geographics

    2 publications · 114 citations

    2

    h-index

1–5