Bayesian State Estimation
Bayesian State Estimation is a statistical framework for state estimation that uses Bayesian inference to recursively update the probability distribution of a system's state by combining prior knowledge with observed evidence, distinguishing itself from other estimation methods through its principled incorporation of uncertainty via Bayes' theorem.
183
Publications
11.8K
Citations
509
Authors
240
Institutions
Publications per year
2017–2026
61
183
A mixed-state condensation tracker with automatic model-switching
Michael Isard, Andrew Blake · 2002 · 324 citations
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Cornell University
Ithaca, United States
7 h-index
14 publications
813 citations

Imperial College London
London, United Kingdom
6 h-index
12 publications
701 citations

University of Alberta
Edmonton, Canada
6 h-index
12 publications
829 citations
University of California, Berkeley
Berkeley, United States
5 h-index
15 publications
630 citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
4 h-index
11 publications
249 citations
Jiangnan University
Wuxi, China
4 h-index
10 publications
462 citations

Zhejiang University
Hangzhou, China
4 h-index
9 publications
322 citations
Stanford University
Stanford, United States
3 h-index
7 publications
178 citations
1–8 of 240
9 publications · 2.2K citations
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Repository · 7 publications · 206 citations
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7 publications · 2.3K citations
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4 publications · 368 citations
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MSSP · 4 publications · 139 citations
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