Particle Smoothing

Particle Smoothing is a sequential Bayesian inference method that approximates the joint posterior distribution of latent states over a complete observation sequence using a set of weighted particles, distinguished from filtering by incorporating future observations to refine past state estimates.

73

Publications

3.7K

Citations

158

Authors

112

Institutions

Publications per year

2017–2026

11

Authors

158

  1. SG
    Simon Godsill

    University of Cambridge, United Kingdom

    11 publications · 902 citations

    11

    h-index

  2. JO
    Jimmy Olsson

    Laboratoire Traitement et Communication de l’Information, France

    6 publications · 286 citations

    6

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  3. PB
    Pete Bunch

    University of Cambridge, United Kingdom

    5 publications · 116 citations

    5

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  4. TB
    Thomas B. Schön

    Linköping University, Sweden

    5 publications · 122 citations

    5

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  5. AD
    Arnaud Doucet

    University of Cambridge, United Kingdom

    4 publications · 847 citations

    4

    h-index

1–5 of 158

Venues

  1. IEEE Transactions on Signal Processing

    8 publications · 264 citations

    8

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  2. arXiv (Cornell University)

    Repository · 4 publications · 93 citations

    4

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  3. 3

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  4. Bernoulli

    2 publications · 110 citations

    2

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  5. Computational Mechanics

    1 publications · 103 citations

    1

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1–5