Likelihood-based Computation

Likelihood-Based Computation is a computational methodology within statistical inference that specifically involves the use of likelihood functions—the probability of observed data given model parameters—to derive or estimate parameters, perform hypothesis testing, or conduct model selection through systematic algorithmic procedures.

252

Publications

31.1K

Citations

5.7K

Authors

632

Institutions

Publications per year

2017–2026

55

Authors

5.7K

  1. SC
    Shyi‐Ming Chen

    National Taiwan University of Science and Technology, Taiwan

    4 publications · 243 citations

    4

    h-index

  2. TC
    Ting‐Yu Chen

    Chang Gung University, Taiwan

    4 publications · 113 citations

    4

    h-index

  3. PJ
    Peter J. Diggle

    Lancaster University, United Kingdom

    4 publications · 574 citations

    4

    h-index

  4. EB
    Enrico Bocchieri

    AT&T (United States), United States

    3 publications · 204 citations

    3

    h-index

  5. SL
    Shili Lin

    The Ohio State University, United States

    3 publications · 60 citations

    3

    h-index

1–5 of 5.7K

Venues

  1. Bioinformatics

    9 publications · 568 citations

    9

    h-index

  2. arXiv (Cornell University)

    Repository · 9 publications · 2.3K citations

    9

    h-index

  3. Molecular Biology and Evolution

    MBE · 7 publications · 2.4K citations

    7

    h-index

  4. PLoS Computational Biology

    5 publications · 371 citations

    5

    h-index

  5. 5

    h-index

1–5