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
Parents
161
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals
Yantao Shen, Tong Xiao, Hongsheng Li et al. · 2017 · 314 citations
META-ANALYSIS OF ANIMAL MOVEMENT USING STATE-SPACE MODELS
Ian D. Jonsen, Ransom A. Myers, Joanna Mills Flemming · Ecology · 2003 · 292 citations
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255
University of Missouri
Columbia, United States
6 h-index
8 publications
612 citations
University of Sheffield
Sheffield, United Kingdom
6 h-index
10 publications
893 citations

University of Edinburgh
Edinburgh, United Kingdom
5 h-index
6 publications
376 citations

Central South University
Changsha, China
4 h-index
6 publications
185 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
8 publications
315 citations
Dalhousie University
Halifax, Canada
4 h-index
9 publications
1.2K citations

City University of Hong Kong
Hong Kong, Hong Kong
3 h-index
6 publications
338 citations
NOAA National Marine Fisheries Service Alaska Fisheries Science Center
Seattle, United States
3 h-index
3 publications
528 citations
1–8 of 255
Repository · 5 publications · 161 citations
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4 publications · 636 citations
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3 publications · 91 citations
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3 publications · 206 citations
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3 publications · 61 citations
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