Bayesian Hidden Markov Models

Bayesian Hidden Markov Models is a probabilistic framework within Bayesian latent variable models that extends the standard Hidden Markov Model by treating model parameters as random variables subject to prior distributions, thereby enabling full posterior inference over both latent states and parameters through Bayesian updating.

125

Publications

4.9K

Citations

344

Authors

202

Institutions

Publications per year

2017–2026

30

Authors

344

  1. LB
    Lukáš Burget

    Brno University of Technology, Czechia

    5 publications · 103 citations

    5

    h-index

  2. MD
    Mireia Díez

    University of the Basque Country, Spain

    4 publications · 88 citations

    4

    h-index

  3. FL
    Federico Landini

    Brno University of Technology, Czechia

    4 publications · 88 citations

    4

    h-index

  4. JC
    Jen‐Tzung Chien

    National Yang Ming Chiao Tung University, Taiwan

    3 publications · 73 citations

    3

    h-index

  5. XS
    Xinyuan Song

    Chinese University of Hong Kong, Hong Kong SAR China

    3 publications · 59 citations

    3

    h-index

1–5 of 344

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 326 citations

    8

    h-index

  2. 5

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 3 publications · 51 citations

    3

    h-index

  4. Statistical Methods in Medical Research

    3 publications · 74 citations

    3

    h-index

  5. Statistics in Medicine

    2 publications · 66 citations

    2

    h-index

1–5