Sequential Importance Resampling is a sequential Monte Carlo method that extends importance sampling by incorporating a resampling step to sequentially mitigate sample degeneracy in particle filters through the replacement of low-weight particles with high-weight duplicates.
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2017–2026
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108
Filtering via Simulation: Auxiliary Particle Filters
M. Pitt, Neil Shephard · Journal of the American Statistical Association · 1999 · 2.3K citations · Full text
Computing, Physics, Statistics +13
Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas et al. · 2000 · 1.4K citations
1–5 of 108
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1–5 of 277
Institutions
144
Stanford University
Stanford, United States
6 h-index
7 publications
597 citations
Duke University
Durham, United States
6 h-index
11 publications
1.3K citations
University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
7 publications
159 citations
Imperial College London
London, United Kingdom
4 h-index
4 publications
3.3K citations
University of Oxford
Oxford, United Kingdom
4 h-index
4 publications
3.3K citations
University of Cambridge
Cambridge, United Kingdom
4 h-index
7 publications
2.4K citations
University of California, Berkeley
Berkeley, United States
4 h-index
5 publications
1.6K citations
Tsinghua University
Beijing, China
3 h-index
12 publications
236 citations
1–8 of 144
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- Journal of the American Statistical Association
7 publications · 4K citations
7
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- IEEE Transactions on Aerospace and Electronic Systems
4 publications · 113 citations
4
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- Structural Safety
3 publications · 426 citations
3
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- IEEE Access
3 publications · 81 citations
3
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- Quarterly Journal of the Royal Meteorological Society
2 publications · 106 citations
2
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