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
Parents
307
1–5 of 307
676
16
h-index
14
h-index
9
h-index
University of North Carolina at Chapel Hill, United States
8 publications · 2.1K citations
8
h-index
6
h-index
1–5 of 676
347

University of Toronto
Toronto, Canada
11 h-index
15 publications
2.7K citations
Leiden University
Leiden, The Netherlands
10 h-index
16 publications
1.6K citations

University of Warwick
Coventry, United Kingdom
10 h-index
16 publications
1K citations
University of Southern California
Los Angeles, United States
9 h-index
16 publications
1.6K citations
Lancaster University
Lancaster, United Kingdom
9 h-index
12 publications
1.5K citations
Massachusetts Institute of Technology
Cambridge, United States
8 h-index
19 publications
1.1K citations
Stanford University
Stanford, United States
7 h-index
12 publications
709 citations

University of Manchester
Manchester, United Kingdom
7 h-index
9 publications
296 citations
1–8 of 347
16 publications · 2.5K citations
14
h-index
Repository · 8 publications · 316 citations
8
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PRL · 8 publications · 725 citations
8
h-index
7 publications · 2.1K citations
7
h-index
5 publications · 264 citations
5
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