Particle Gibbs Sampling

Particle Gibbs Sampling is a Markov chain Monte Carlo algorithm that belongs to the class of particle MCMC methods, distinguished by its integration of a conditional sequential Monte Carlo update within a Gibbs sampling framework to sample from the full posterior distribution of latent states in state-space models.

38

Publications

1.4K

Citations

75

Authors

50

Institutions

Publications per year

2017–2026

7

Variants

Pgibbs

Authors

75

  1. TB
    Thomas B. Schön

    Linköping University, Sweden

    7 publications · 387 citations

    7

    h-index

  2. FL
    Fredrik Lindsten

    Linköping University, Sweden

    7 publications · 411 citations

    7

    h-index

  3. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    3 publications · 286 citations

    3

    h-index

  4. VK
    Vikash K. Mansinghka

    Massachusetts Institute of Technology, United States

    2 publications · 26 citations

    2

    h-index

  5. JV
    Jan-Willem van de Meent

    University of Oxford, United Kingdom

    2 publications · 26 citations

    2

    h-index

1–5 of 75

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 228 citations

    6

    h-index

  2. Bernoulli

    3 publications · 135 citations

    3

    h-index

  3. IFAC Proceedings Volumes

    2 publications · 36 citations

    2

    h-index

  4. Oxford University Research Archive (ORA) (University of Oxford)

    Repository · 2 publications · 21 citations

    2

    h-index

  5. Cambridge University Engineering Department Publications Database

    Repository · 2 publications · 94 citations

    2

    h-index

1–5