Variable-length Hidden Markov Models

Variable-Length Hidden Markov Models is a type of probabilistic sequence model that extends the standard Hidden Markov Model by allowing state durations to follow variable-length distributions, thereby capturing temporal dependencies with non-geometric duration densities.

51

Publications

2.7K

Citations

144

Authors

69

Institutions

Publications per year

2017–2026

2

Variants

Variable-length HmmsVlhmm

Authors

144

  1. AG
    Aphrodite Galata

    University of Manchester, United Kingdom

    9 publications · 495 citations

    9

    h-index

  2. DH
    David Hogg

    University of Leeds, United Kingdom

    3 publications · 320 citations

    3

    h-index

  3. NJ
    Neil Johnson

    University of Leeds, United Kingdom

    3 publications · 320 citations

    3

    h-index

  4. SŞ
    Sertan Şentürk

    Universitat Pompeu Fabra, Spain

    2 publications · 40 citations

    2

    h-index

  5. RH
    Roger Hubbold

    University of Manchester, United Kingdom

    2 publications · 35 citations

    2

    h-index

1–5 of 144

Venues

  1. Computer Vision and Image Understanding

    CVIU · 2 publications · 287 citations

    2

    h-index

  2. IEEE Transactions on Multimedia

    2 publications · 28 citations

    2

    h-index

  3. PLoS Computational Biology

    2 publications · 50 citations

    2

    h-index

  4. Bioinformatics

    2 publications · 69 citations

    2

    h-index

  5. Journal of Computational Biology

    2 publications · 122 citations

    2

    h-index

1–5