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The Monte Carlo Method
Abstract
We shall present here the motivation and a general description of a method dealing with a class of problems in mathematical physics. The method is, essentially, a statistical approach to the study of differential equations, or more generally, of integro-differential equations that occur in various branches of the natural sciences.
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“Frequently, the study of thermodynamical properties and observables, such as adsorption isotherms, Henry constants, heats of adsorption, is carried out via Monte Carlo simulations, which are based on statistical mechanics.344−346”
From Zeolites in Adsorption Processes: State of the Art and Future Prospects (2022) · cites this paper as [344]
In the citing paper
“Because bootstrapping of standard errors of the indirect effects was not possible with Lavaan, con fi dence intervals (95%) for indirect effects in the multiple imputed datasets were estimated using the Monte Carlo sampling method (Metropolis & Ulam, 1949).”
From Mindfulness, Self-compassion, and Psychological Wellbeing as Correlates of Paternal Bonding in Pregnancy. (2025) · cites this paper as Metropolis & Ulam, 1949
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Intelligent Textiles and clothing
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Electron Correlation Effects in Molecules
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Mean Field Simulation for Monte Carlo Integration
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“The Monte Carlo approach (Metropolis and Ulam 1949; Shonkwiler and Mendivil 2009) can be used as an alternative way for constructing the probability distribution of Y in case the mathematical approach is too hard.”
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“In the early 1960s as a professor at University of Michigan, Edward Epstein began to use Monte Carlo methods, probabilistic methods that flourished in the post–World War II computer age at places like Los Alamos National Laboratory (Metropolis and Ulam 1949).”
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The Kinetic Monte Carlo method: Foundation, implementation, and application
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Metadata
- Authors
- Metropolis, N., & Ulam, S.
- Affiliations
- Los Alamos National Laboratory (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1949-09
- Volume
- 44
- Issue
- 247
- Pages
- 335-341
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2051203581
- Cited by
- 6,474
- References
- 0
- Citations by year
- 2026: 368 · 2025: 419 · 2024: 327 · 2023: 345 · 2022: 333 · 2021: 343 · 2020: 318 · 2019: 304 · 2018: 270 · 2017: 252 · 2016: 258 · 2015: 249 · 2014: 254 · 2013: 283 · 2012: 262
- FWCI
- 8.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Mathematics › Numerical Analysis › Mathematical Approximation and Integration
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Keywords
- Monte Carlo methods, integro-differential equations, mathematical physics, numerical solution of partial differential equations
- MeSH terms
- Biometry, Humans, Monte Carlo Method, Statistics as Topic
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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