Adaptive Metropolis Algorithms

Adaptive Metropolis Algorithms is a Markov chain Monte Carlo method that iteratively refines its proposal distribution during sampling by dynamically adjusting the covariance structure of the proposal based on accumulated posterior samples to improve convergence and mixing in Bayesian inference.

139

Publications

16.2K

Citations

338

Authors

189

Institutions

Publications per year

2017–2026

37

Variants

Adaptive Metropolis Algorithm

Authors

338

  1. JA
    Jasper A. Vrugt

    University of California, Irvine, United States

    14 publications · 2.3K citations

    14

    h-index

  2. JS
    Jeffrey S. Rosenthal

    University of Toronto, Canada

    5 publications · 1.7K citations

    5

    h-index

  3. HV
    Harry Vereecken

    Forschungszentrum Jülich, Germany

    5 publications · 263 citations

    5

    h-index

  4. MV
    Matti Vihola

    University of Jyväskylä, Finland

    5 publications · 427 citations

    5

    h-index

  5. HH
    Heikki Haario

    Lappeenranta-Lahti University of Technology, Finland

    5 publications · 4.4K citations

    5

    h-index

1–5 of 338

Venues

  1. Water Resources Research

    13 publications · 2.6K citations

    13

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  2. Statistics and Computing

    7 publications · 2.9K citations

    7

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 5 publications · 165 citations

    5

    h-index

  4. Journal of Computational and Graphical Statistics

    JCGS · 4 publications · 1.2K citations

    4

    h-index

  5. 4

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1–5