Optimal Scaling

Optimal Scaling is a methodological framework within Markov Chain Monte Carlo theory that identifies the asymptotically optimal rate of convergence for proposal distributions by characterizing the scaling of step sizes in high-dimensional random walk Metropolis algorithms.

307

Publications

21.4K

Citations

676

Authors

347

Institutions

Publications per year

2017–2026

68

Authors

676

  1. GO
    Gareth O. Roberts

    University of Warwick, United Kingdom

    18 publications · 5.1K citations

    16

    h-index

  2. JD
    Jan de Leeuw

    University of California, Los Angeles, United States

    16 publications · 2.4K citations

    14

    h-index

  3. JS
    Jeffrey S. Rosenthal

    University of Toronto, Canada

    9 publications · 3.4K citations

    9

    h-index

  4. FW
    Forrest W. Young

    University of North Carolina at Chapel Hill, United States

    8 publications · 2.1K citations

    8

    h-index

  5. YT
    Yoshio Takane

    McGill University, Canada

    6 publications · 1.6K citations

    6

    h-index

1–5 of 676

Venues

  1. Psychometrika

    16 publications · 2.5K citations

    14

    h-index

  2. arXiv (Cornell University)

    Repository · 8 publications · 316 citations

    8

    h-index

  3. Physical Review Letters

    PRL · 8 publications · 725 citations

    8

    h-index

  4. The Annals of Applied Probability

    7 publications · 2.1K citations

    7

    h-index

  5. IEEE Transactions on Signal Processing

    5 publications · 264 citations

    5

    h-index

1–5