Bayesian Covariance Modeling

Bayesian Covariance Modeling is a statistical framework within spatial covariance modeling that incorporates prior distributions over covariance parameters to enable probabilistic inference and uncertainty quantification in the estimation of spatial dependence structures.

15

Publications

643

Citations

35

Authors

25

Institutions

Publications per year

2017–2026

2

Authors

35

  1. PG
    Peter Guttorp

    University of Washington, United States

    2 publications · 162 citations

    2

    h-index

  2. MS
    Mickael Salabasis

    Stockholm School of Economics, Sweden

    1 publications · 11 citations

    1

    h-index

  3. AM
    Alexandra M. Schmidt

    Universidade Federal do Rio de Janeiro, Brazil

    1 publications · 48 citations

    1

    h-index

  4. DR
    David Ruppert

    Cornell University, United States

    1 publications · 70 citations

    1

    h-index

  5. SC
    Siddhartha Chib

    Washington University in St. Louis, United States

    1 publications · 10 citations

    1

    h-index

1–5 of 35

Venues

  1. 1

    h-index

  2. Psychometrika

    1 publications · 27 citations

    1

    h-index

  3. Statistical Methodology

    1 publications · 17 citations

    1

    h-index

  4. RePEc: Research Papers in Economics

    Repository · 1 publications · 10 citations

    1

    h-index

  5. Environmetrics

    1 publications · 114 citations

    1

    h-index

1–5