Bayesian Sequence Models

Bayesian Sequence Models is a class of probabilistic sequence modeling frameworks that integrate Bayesian inference to quantify uncertainty in predictions by treating model parameters as random variables with prior distributions updated through observed sequential data.

21

Publications

646

Citations

76

Authors

61

Institutions

Publications per year

2017–2026

6

Authors

76

  1. GD
    G.A.T. Duller

    Aberystwyth University, United Kingdom

    2 publications · 90 citations

    2

    h-index

  2. MJ
    Matthew J. Burke

    University of Liverpool, United Kingdom

    2 publications · 90 citations

    2

    h-index

  3. JG
    Jan Gasthaus

    Amazon (United States), United States

    2 publications · 80 citations

    2

    h-index

  4. SD
    Stanislas Dehaene

    Inserm, France

    2 publications · 160 citations

    2

    h-index

  5. YW
    Yee Whye Teh

    University of Oxford, United Kingdom

    2 publications · 80 citations

    2

    h-index

1–5 of 76

Venues

  1. Geology

    1 publications · 48 citations

    1

    h-index

  2. 1

    h-index

  3. Journal of Computational Biology

    1 publications · 19 citations

    1

    h-index

  4. Journal of Cleaner Production

    1 publications · 17 citations

    1

    h-index

  5. Bioinformation

    1 publications · 23 citations

    1

    h-index

1–5