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Monte Carlo simulations: Hidden errors from ‘‘good’’ random number generators
Abstract
The Wolff algorithm is now accepted as the best cluster-flipping Monte Carlo algorithm for beating ``critical slowing down.'' We show how this method can yield incorrect answers due to subtle correlations in ``high quality'' random number generators.
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“A.M. Ferrenberg et al.15 identified hidden systematic errors in Monte Carlo simulations when using Pseudo Random Number Generators (PRNGs), such as CONG, R250, R1279, SWC and SWCW. Although these generators successfully passed standard statistical randomness tests, they exhibited hidden correlations and significant biases when applied to real-world simulations.”
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Metadata
- Authors
- Ferrenberg, A. M., Landau, D. P., & Wong, Y. J.
- Affiliations
- University of Georgia (US)
- IBM (United States) (US)
- ORCID
- Landau, D. P.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 1992-12-07
- Volume
- 69
- Issue
- 23
- Pages
- 3382-3384
- ISSN
- 0031-9007 (ISSN-L)
- OpenAlex
- W2090824272
- Cited by
- 437
- References
- 9
- Citations by year
- 2026: 9 · 2025: 3 · 2024: 10 · 2023: 12 · 2022: 5 · 2021: 9 · 2020: 9 · 2019: 12 · 2018: 10 · 2017: 44 · 2016: 5 · 2015: 10 · 2014: 9 · 2013: 9 · 2012: 9
- FWCI
- 11.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Keywords
- random number generators, Wolff cluster algorithm, critical slowing down
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