Metropolis-adjusted Langevin Algorithm

The Metropolis-Adjusted Langevin Algorithm is a Markov chain Monte Carlo method that combines Langevin dynamics with the Metropolis-Hastings correction to ensure convergence to the target distribution by proposing moves biased toward regions of higher probability density via gradient information.

37

Publications

4.4K

Citations

79

Authors

60

Institutions

Publications per year

2017–2026

16

Authors

79

  1. MG
    Mark Girolami

    University of Glasgow, United Kingdom

    4 publications · 1.7K citations

    4

    h-index

  2. GO
    Gareth O. Roberts

    University of Warwick, United Kingdom

    4 publications · 1.3K citations

    4

    h-index

  3. WT
    Wilson Tsakane Mongwe

    University of Johannesburg, South Africa

    3 publications · 47 citations

    3

    h-index

  4. RM
    Rendani Mbuvha

    University of the Witwatersrand, South Africa

    3 publications · 47 citations

    3

    h-index

  5. ÉM
    Éric Moulines

    Télécom Paris, France

    3 publications · 79 citations

    3

    h-index

1–5 of 79

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 156 citations

    5

    h-index

  2. The Annals of Applied Probability

    3 publications · 129 citations

    3

    h-index

  3. IEEE Transactions on Signal Processing

    2 publications · 79 citations

    2

    h-index

  4. 2

    h-index

  5. Statistics and Computing

    2 publications · 187 citations

    2

    h-index

1–5