Metropolis-hastings Algorithm

The Metropolis-Hastings Algorithm is a Markov chain Monte Carlo method that generates samples from a probability distribution by constructing a Markov chain with a specified stationary distribution, using a proposal distribution and an acceptance criterion to ensure convergence to the target distribution.

1K

Publications

110.2K

Citations

2.1K

Authors

936

Institutions

Publications per year

2017–2026

249

Variants

Metropolis-hastingsMetropolis-hastings Method

Authors

2.1K

  1. GO
    Gareth O. Roberts

    University of Warwick, United Kingdom

    37 publications · 9.5K citations

    26

    h-index

  2. JS
    Jeffrey S. Rosenthal

    University of Toronto, Canada

    17 publications · 4.5K citations

    15

    h-index

  3. RM
    Radford M. Neal

    University of Toronto, Canada

    11 publications · 5.4K citations

    11

    h-index

  4. SC
    Siddhartha Chib

    Washington University in St. Louis, United States

    10 publications · 7K citations

    10

    h-index

  5. AD
    Arnaud Doucet

    University of Cambridge, United Kingdom

    10 publications · 886 citations

    10

    h-index

1–5 of 2.1K

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 41 publications · 7.8K citations

    29

    h-index

  2. arXiv (Cornell University)

    Repository · 50 publications · 2.4K citations

    21

    h-index

  3. Journal of the American Statistical Association

    23 publications · 5.8K citations

    21

    h-index

  4. The Annals of Applied Probability

    21 publications · 3.4K citations

    20

    h-index

  5. The Journal of Chemical Physics

    14 publications · 663 citations

    12

    h-index

1–5