Hidden-state Models

Hidden-State Models is a statistical framework that belongs to the class of latent variable models, distinguished by its specification of unobservable (hidden) states that stochastically generate observable data through defined transition and emission probabilities, enabling inference of ancestral states, transcriptional dynamics, or other latent biological processes from indirect or noisy measurements.

860

Publications

66.4K

Citations

2.6K

Authors

904

Institutions

Publications per year

2017–2026

345

Authors

2.6K

  1. YB
    Yoshua Bengio

    Université de Montréal, Canada

    8 publications · 996 citations

    8

    h-index

  2. YH
    Yulan He

    Aston University, United Kingdom

    6 publications · 308 citations

    6

    h-index

  3. SY
    Steve Young

    University of Cambridge, United Kingdom

    5 publications · 779 citations

    5

    h-index

  4. JS
    Jinsong Su

    Xiamen University, China

    5 publications · 214 citations

    5

    h-index

  5. TD
    Trevor Darrell

    University of California, Berkeley, United States

    5 publications · 1.4K citations

    5

    h-index

1–5 of 2.6K

Venues

  1. arXiv (Cornell University)

    Repository · 63 publications · 3.8K citations

    27

    h-index

  2. IEEE Transactions on Signal Processing

    17 publications · 959 citations

    15

    h-index

  3. IEEE Access

    10 publications · 782 citations

    10

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 10 publications · 762 citations

    10

    h-index

  5. Physical review. A/Physical review, A

    PRA · 9 publications · 454 citations

    9

    h-index

1–5