Stochastic State-space Models

Stochastic State-Space Models is a state-space representation that incorporates random disturbances in both the state transition and observation processes, distinguishing it from deterministic models by explicitly modeling uncertainty through probabilistic dynamics.

420

Publications

22.1K

Citations

925

Authors

482

Institutions

Publications per year

2017–2026

81

Variants

Stochastic State Space Models

Authors

925

  1. ZW
    Zidong Wang

    Brunel University of London, United Kingdom

    7 publications · 504 citations

    7

    h-index

  2. TK
    T. Kailath

    Stanford University, United States

    6 publications · 432 citations

    6

    h-index

  3. JM
    J.B. Moore

    Australian National University, Australia

    4 publications · 119 citations

    4

    h-index

  4. PS
    Peng Shi

    The University of Adelaide, Australia

    4 publications · 246 citations

    4

    h-index

  5. AA
    Alessandro Abate

    University of Oxford, United Kingdom

    4 publications · 119 citations

    4

    h-index

1–5 of 925

Venues

  1. IEEE Transactions on Automatic Control

    26 publications · 3.1K citations

    21

    h-index

  2. Automatica

    14 publications · 883 citations

    13

    h-index

  3. SSRN Electronic Journal

    Repository · 8 publications · 200 citations

    8

    h-index

  4. Biometrika

    7 publications · 508 citations

    7

    h-index

  5. arXiv (Cornell University)

    Repository · 7 publications · 607 citations

    7

    h-index

1–5