Parameter Estimation Design

Parameter Estimation Design is a Bayesian experimental design strategy that specifies the selection of experimental conditions or observations to maximize the precision and accuracy of posterior parameter inference in a statistical model.

57

Publications

2.5K

Citations

170

Authors

106

Institutions

Publications per year

2017–2026

16

Variants

Optimal Experimental Design

Authors

170

  1. WG
    William G. Hunter

    University of Wisconsin–Madison, United States

    3 publications · 211 citations

    3

    h-index

  2. SW
    Stewart W. Franks

    University of Newcastle Australia, Australia

    2 publications · 786 citations

    2

    h-index

  3. RR
    Reza Rezaie

    University of New Orleans, United States

    2 publications · 29 citations

    2

    h-index

  4. WJ
    William J. Hill

    University of Wisconsin–Madison, United States

    2 publications · 60 citations

    2

    h-index

  5. XR
    X. Rong Li

    University of New Orleans, United States

    2 publications · 29 citations

    2

    h-index

1–5 of 170

Venues

  1. Chemical Engineering Science

    3 publications · 78 citations

    3

    h-index

  2. Journal of Hydrology

    3 publications · 811 citations

    3

    h-index

  3. Technometrics

    2 publications · 60 citations

    2

    h-index

  4. Journal of Statistical Planning and Inference

    2 publications · 55 citations

    2

    h-index

  5. Biometrika

    2 publications · 183 citations

    2

    h-index

1–5