Probabilistic Assignment

Probabilistic Assignment is a statistical inference method used in mixed-stock analysis that assigns individuals to potential source populations based on the likelihood of their genetic or phenotypic markers, weighted by the relative contributions of each population to the mixed sample.

49

Publications

3.8K

Citations

110

Authors

74

Institutions

Publications per year

2017–2026

11

Authors

110

  1. EJ
    Eun Jeong Heo

    University of Rochester, United States

    4 publications · 131 citations

    4

    h-index

  2. AB
    Arash Bahrami

    University of Wisconsin–Madison, United States

    2 publications · 217 citations

    2

    h-index

  3. JL
    John L. Markley

    University of Wisconsin–Madison, United States

    2 publications · 217 citations

    2

    h-index

  4. HR
    Hamid R. Eghbalnia

    University of Wisconsin–Madison, United States

    2 publications · 217 citations

    2

    h-index

  5. YK
    Yoichi Kasajima

    UNSW Sydney, Australia

    2 publications · 45 citations

    2

    h-index

1–5 of 110

Venues

  1. Social Choice and Welfare

    4 publications · 72 citations

    4

    h-index

  2. Ecological Applications

    3 publications · 347 citations

    3

    h-index

  3. Transportation Science

    3 publications · 970 citations

    3

    h-index

  4. Journal of Heredity

    2 publications · 88 citations

    2

    h-index

  5. Journal of Mathematical Economics

    2 publications · 80 citations

    2

    h-index

1–5