Bayesian Concordance Analysis

Bayesian Concordence Analysis is a statistical framework for species tree inference that estimates the posterior distribution of gene trees and species trees by modeling the concordance among genealogical histories under a Bayesian hierarchical model.

26

Publications

3.1K

Citations

121

Authors

79

Institutions

Publications per year

2017–2026

4

Variants

Bca

Authors

121

  1. CA
    Cécile Ané

    University of Wisconsin–Madison, United States

    5 publications · 1.2K citations

    5

    h-index

  2. DB
    David Baum

    University of Wisconsin–Madison, United States

    4 publications · 853 citations

    4

    h-index

  3. BL
    Bret Larget

    University of Wisconsin–Madison, United States

    2 publications · 580 citations

    2

    h-index

  4. CN
    Colin N. Dewey

    University of Wisconsin–Madison, United States

    2 publications · 580 citations

    2

    h-index

  5. BL
    B. Larget

    Duke Energy (United States), United States

    2 publications · 571 citations

    2

    h-index

1–5 of 121

Venues

  1. PLoS ONE

    4 publications · 216 citations

    4

    h-index

  2. Systematic Biology

    3 publications · 786 citations

    3

    h-index

  3. Molecular Biology and Evolution

    MBE · 2 publications · 571 citations

    2

    h-index

  4. PLoS Genetics

    2 publications · 161 citations

    2

    h-index

  5. Bioinformatics

    1 publications · 465 citations

    1

    h-index

1–5