Gaussian Process Filtering

Gaussian Process Filtering is a Bayesian filtering method that employs Gaussian processes as prior distributions over functions to recursively estimate the state of a dynamic system from noisy observations.

156

Publications

9.5K

Citations

463

Authors

212

Institutions

Publications per year

2017–2026

74

Variants

Gp Filtering

Authors

463

  1. SR
    Stephen Roberts

    University of Oxford, United Kingdom

    8 publications · 1.2K citations

    8

    h-index

  2. JK
    Jonathan Ko

    University of Washington, United States

    5 publications · 606 citations

    5

    h-index

  3. SR
    Steven Reece

    University of Oxford, United Kingdom

    5 publications · 758 citations

    5

    h-index

  4. MA
    Michael A. Osborne

    University of Oxford, United Kingdom

    5 publications · 1.1K citations

    5

    h-index

  5. BC
    Brahim Chaib-draa

    Université Laval, Canada

    4 publications · 116 citations

    4

    h-index

1–5 of 463

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 226 citations

    5

    h-index

  2. 4

    h-index

  3. Journal of Computational Physics

    4 publications · 824 citations

    4

    h-index

  4. Expert Systems with Applications

    3 publications · 86 citations

    3

    h-index

  5. Mechanical Systems and Signal Processing

    MSSP · 3 publications · 71 citations

    3

    h-index

1–5