Spatio-temporal State-space Models

Spatio-Temporal State-Space Models is a statistical modeling framework that extends traditional state-space models by explicitly incorporating both spatial dependencies and temporal dynamics in the latent state and observation processes, enabling the joint inference of unobserved spatio-temporal phenomena from noisy and partial observations.

161

Publications

9.6K

Citations

492

Authors

255

Institutions

Publications per year

2017–2026

58

Authors

492

  1. CK
    Christopher K. Wikle

    University of Missouri, United States

    5 publications · 439 citations

    5

    h-index

  2. NC
    Noel Cressie

    Iowa State University, United States

    4 publications · 204 citations

    4

    h-index

  3. VK
    Visakan Kadirkamanathan

    University of Sheffield, United Kingdom

    4 publications · 221 citations

    4

    h-index

  4. MD
    Michael Dewar

    University of Sheffield, United Kingdom

    4 publications · 221 citations

    4

    h-index

  5. TM
    Théo Michelot

    University of St Andrews, United Kingdom

    3 publications · 518 citations

    3

    h-index

1–5 of 492

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 161 citations

    5

    h-index

  2. Methods in Ecology and Evolution

    4 publications · 636 citations

    4

    h-index

  3. IEEE Transactions on Signal Processing

    3 publications · 91 citations

    3

    h-index

  4. Ecological Informatics

    3 publications · 206 citations

    3

    h-index

  5. 3

    h-index

1–5