Bayesian State-space Inference

Bayesian State-Space Inference is a statistical framework for estimating the posterior distribution of latent states in a state-space model by combining prior knowledge with observed data through Bayes' theorem, distinguishing itself from classical inference methods by explicitly incorporating parameter and state uncertainty in a probabilistic manner.

198

Publications

14.1K

Citations

639

Authors

342

Institutions

Publications per year

2017–2026

68

Authors

639

  1. OG
    Olivier Giménez

    Centre d'Écologie Fonctionnelle et Évolutive, France

    4 publications · 151 citations

    4

    h-index

  2. TB
    Thomas B. Schön

    Linköping University, Sweden

    4 publications · 127 citations

    4

    h-index

  3. BH
    Biao Huang

    University of Alberta, Canada

    4 publications · 196 citations

    4

    h-index

  4. XM
    Xue Mei

    Temple University, United States

    3 publications · 1.9K citations

    3

    h-index

  5. SZ
    Shunyi Zhao

    Jiangnan University, China

    3 publications · 177 citations

    3

    h-index

1–5 of 639

Venues

  1. 10

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  2. arXiv (Cornell University)

    Repository · 11 publications · 285 citations

    10

    h-index

  3. PLoS ONE

    6 publications · 236 citations

    6

    h-index

  4. Ecology

    5 publications · 479 citations

    5

    h-index

  5. Ecological Applications

    5 publications · 175 citations

    5

    h-index

1–5