Continuous Observation Models

Continuous Observation Models is a type of emission probability model in Hidden Markov Models that specifies the likelihood of observing continuous-valued signals by using parametric distributions such as Gaussian mixtures, as opposed to discrete symbols in finite observation spaces.

95

Publications

5.3K

Citations

253

Authors

123

Institutions

Publications per year

2017–2026

14

Variants

ChmmContinuous Hidden Markov Models

Authors

253

  1. MS
    M. S. Phadke

    University of Wisconsin–Madison, United States

    2 publications · 93 citations

    2

    h-index

  2. MF
    Meir Feder

    Tel Aviv University, Israel

    2 publications · 114 citations

    2

    h-index

  3. PJ
    Peter J. Bickel

    University of California, Berkeley, United States

    2 publications · 126 citations

    2

    h-index

  4. VP
    V. P. Belavkin

    University of Nottingham, United Kingdom

    2 publications · 210 citations

    2

    h-index

  5. NM
    Neri Merhav

    Technion – Israel Institute of Technology, Israel

    2 publications · 114 citations

    2

    h-index

1–5 of 253

Venues

  1. Physical Review A

    PRA · 3 publications · 214 citations

    3

    h-index

  2. Biometrics

    2 publications · 89 citations

    2

    h-index

  3. 2

    h-index

  4. 2

    h-index

  5. PubMed

    Repository · 2 publications · 35 citations

    2

    h-index

1–5