Maximum Likelihood Estimation Algorithms

Maximum Likelihood Estimation Algorithms is a computational methodology in phylogenetics that, within the framework of statistical inference, uses iterative optimization techniques to identify the phylogenetic tree and model parameters that maximize the probability of observing the given sequence data under a specified evolutionary model.

1.1K

Publications

302K

Citations

2.6K

Authors

1K

Institutions

Publications per year

2017–2026

200

Authors

2.6K

  1. AS
    Alexandros Stamatakis

    Heidelberg Institute for Theoretical Studies, Germany

    18 publications · 55.4K citations

    15

    h-index

  2. BO
    Broderick O. Oluyede

    Georgia Southern University, United States

    9 publications · 152 citations

    9

    h-index

  3. TW
    Tandy Warnow

    The University of Texas at Austin, United States

    8 publications · 565 citations

    8

    h-index

  4. ZY
    Ziheng Yang

    University College London, United Kingdom

    8 publications · 5.8K citations

    8

    h-index

  5. OG
    Olivier Gascuel

    Centre National de la Recherche Scientifique, France

    8 publications · 40.8K citations

    8

    h-index

1–5 of 2.6K

Venues

  1. Journal of the American Statistical Association

    31 publications · 3.6K citations

    23

    h-index

  2. IEEE Transactions on Signal Processing

    31 publications · 2.5K citations

    22

    h-index

  3. Bioinformatics

    22 publications · 60.7K citations

    21

    h-index

  4. Systematic Biology

    22 publications · 38.9K citations

    20

    h-index

  5. Molecular Biology and Evolution

    MBE · 21 publications · 51.3K citations

    19

    h-index

1–5