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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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Eddowes, M. H., Mills, T. N., & Delpy, D. T. (1995) · Applied Optics · doi:10.1364/ao.34.002261 · Cited by 38
ANALYTICAL APPLICATIONS OF MONTE CARLO TECHNIQUES
Güell, O. A., & Holcombe, J. A. (1990) · Analytical Chemistry · doi:10.1021/ac00208a714 · Cited by 38
Biology, Buddhism, and AI: Care as the Driver of Intelligence
Doctor, T. H., Witkowski, O., Solomonova, E., Levin, M. G., & Duane, B. (2022) · Entropy · doi:10.3390/e24050710 · Cited by 42
Doubly constrained offline reinforcement learning for learning path recommendation
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Parallel Fission Bank Algorithms in Monte Carlo Criticality Calculations
Romano, P. K., & Forget, B. (2012) · Nuclear Science and Engineering · doi:10.13182/nse10-98 · Cited by 37
“Even in the first paper on the Monte Carlo method [1], John Metropolis and Stanislaw Ulam recognized that solving the Boltzmann equation with the Monte Carlo method could be done in parallel very easily whereas the deterministic counterparts for solving the Boltzmann equation did not offer such a natural means of para…”
MethodSu, Y., Li, X., & Xie, Z. (2010) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2010.11.009 · Cited by 37
Atomdroid: A Computational Chemistry Tool for Mobile Platforms
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Borghese, B., Tost, J., de Surville, M., Busato, F., Letourneur, F., Mondon, F., Vaiman, D., & Chapron, C. (2015) · BioMed Research International · doi:10.1155/2015/461024 · Cited by 42
Severson, K., VanAntwerp, J. G., Natarajan, V., et al. (2015) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2015.05.006 · Cited by 37
Li, J., Wen-zheng, L., Gao, X., et al. (2020) · Environment International · doi:10.1016/j.envint.2020.105462 · Cited by 37
“Monte Carlo simulation was used to perform quantitative uncertainty analysis (Metropolis and Ulam, 1949; Thompson and Graham, 1996), employing as inputs probability distributions on the basis of empirical data.”
MethodLiu, F., Wei, P., Zhou, C., et al. (2020) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2019.12.032 · Cited by 37
“For assessing the small failure probability, the Monte Carlo Simulation (MCS) 11 is commonly inefficient.”
Uncertainties in the Seismic Assessment of Historical Masonry Buildings
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“Though this removes the requirement for keeping the number of analyses low, it is still better to avoid redundant analyses stemming from the application of the classical Monte Carlo method [31], which does not benefit the statistical output.”
MethodFirst-principles-based high-throughput computation for high entropy alloys with short range order
Sorkin, V., Chen, S., Tan, T. L., Yu, Z. G., Man, M., & Zhang, Y. W. (2021) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2021.160776 · Cited by 36
Williams, R. J. (2016) · Critical Inquiry · doi:10.1086/685603 · Cited by 37
An Introduction to Light Interaction with Human Skin
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Development of a Rotamer Library for Use in β-Peptide Foldamer Computational Design
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Carré, M., Sachs, J. P., Wallace, J. M., & Favier, C. (2012) · Climate of the past · doi:10.5194/cp-8-433-2012 · Cited by 37
Modelling the transient effect of 1-MCP on ‘Hass’ avocado softening: A Mexican comparative study
Ochoa-Ascencio, S., Hertog, M., & Nicolaï, B. M. (2008) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2008.06.002 · Cited by 37
“Monte Carlo simlations consist of a large number of simulations using randomly rawn values for the different unknown model parameters from heir expected distributions (Metropolis and Ulam, 1949; Eckhardt, 987).”
BackgroundMONTE-CARLO METHODS IN GLOBAL ILLUMINATION
Szirmay‐Kalos, L. (2000) · Cited by 37
Random-number generation on digital computers
Chambers, R. P. (1967) · IEEE Spectrum · doi:10.1109/mspec.1967.5216203 · Cited by 37
Rockfall Susceptibility Assessment at the Municipal Scale (Bovec Municipality, Slovenia)
Čarman, M., Bavec, M., Komac, M., et al. (2014) · Springer eBooks · doi:10.1007/978-3-319-09057-3_358 · Cited by 36
“Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to obtain numerical results (Metropolis and Ulam 1949).”
MethodKeramidas, I., Dimarchopoulou, D., Ofir, E., Scotti, M., Tsikliras, A. C., & Gal, G. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1182921 · Cited by 41
“Ecosampler is based on a Monte Carlo method (Metropolis and Ulam, 1949), built in EwE for automated input parameter optimization (Kavanagh et al.”
MethodXing, X., Wu, M., Zhang, W., Ju, W., Tagesson, T., He, W., Wang, S., Wang, J., Lu, H., Yuan, S., et al. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109789 · Cited by 36
“…2021; Kaminski et al., 2013; Rayner et al., 2005; Scholze et al., 2016, 2019; Wu et al., 2020a, 2019b), machine learning algorithms and the optimization approaches using the Monte Carlo sampling algorithm (Metropolis and Ulam, 1949; Verbeeck et al., 2006; Wu et al., 2020b, 2019c, 2021, 2011, 2012).”
Optimal design of water supply networks for enhancing seismic reliability
Yoo, D. G., Kang, D., & Kim, J. H. (2015) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2015.10.001 · Cited by 36
Gan, Y., Liang, X., Duan, Q., Choi, H. I., Dai, Y., & Wu, H. (2015) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2014ms000406 · Cited by 36
Multiscale Modeling of Complex Dynamic Problems: An Overview and Recent Developments
Jebahi, M., Dau, F., Charles, J., & Ivan Iordanoff, A. (2014) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-014-9136-6 · Cited by 40
Loving, V. A., Edwards, D. B., Roche, K. T., et al. (2014) · American Journal of Roentgenology · doi:10.2214/ajr.13.11368 · Cited by 36
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Müller, K., & Cheng, P. W. (2018) · Wind energy science · doi:10.5194/wes-3-149-2018 · Cited by 36
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Li, Y.-H., Han, X., Cao, Y., Yuan, X., Li, Z.-P., Guan, J.-Y., Yin, J., Zhang, Q., Ma, X., Peng, C.-Z., et al. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0208-1 · Cited by 39
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Rusakova, I. L. (2022) · Magnetochemistry · doi:10.3390/magnetochemistry8050050 · Cited by 36
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Monte Carlo code for high spatial resolution ocean color simulations
D’Alimonte, D., Zibordi, G., Kajiyama, T., et al. (2010) · Applied Optics · doi:10.1364/ao.49.004936 · Cited by 36
Aggregate formation on polydisperse ferrofluids: A Monte Carlo analysis
Castro, L. L. E., da Silva, M. F., Bakuzis, A. F., et al. (2005) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2005.01.072 · Cited by 36
Wellmann, F. (2013) · Entropy · doi:10.3390/e15041464 · Cited by 36
Characterization of porous materials using molecular theory and simulation
Lastoskie, C. M., & Gubbins, K. E. (2001) · Advances in chemical engineering · doi:10.1016/s0065-2377(01)28007-7 · Cited by 35
Risk-aware decision making and dynamic programming
Defourny, B., Ernst, D., & Wehenkel, L. A. (2008) · ORBi (University of Liège) · Cited by 36
54 Gbps real time quantum random number generator with simple implementation
Yang, J., Liu, J., Su, Q., Li, Z., Fan, F., Xu, B., & Guo, H. (2016) · Optics Express · doi:10.1364/oe.24.027475 · Cited by 39
Direct measurements and path integral Monte Carlo calculations of kinetic energies of solid neon
Timms, D. N., Evans, A. C., Boninsegni, M., et al. (1996) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/8/36/018 · Cited by 36
Gospodarek, T. (2009) · Cited by 35
Gjerde, H., & Verstraete, A. (2010) · Forensic Science International · doi:10.1016/j.forsciint.2009.12.011 · Cited by 35
“A Monte Carlo simulation is a method evaluating a deterministic model using sets of random numbers (or rather, pseudorandom numbers) as inputs, often iteratively [18-19].”
MethodUnified Framework for Training Point Selection and Error Estimation for Surrogate Models
Boopathy, K., & Rumpfkeil, M. P. (2014) · AIAA Journal · doi:10.2514/1.j053064 · Cited by 35
Sassi, M., & Cardaci, A. (2013) · Food Policy · doi:10.1016/j.foodpol.2013.06.002 · Cited by 35
Sensitivity analysis of temperature uncertainty in an aircraft PEM fuel cell
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Deshpande, D., Pasipanodya, J. G., & Gumbo, T. (2016) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.02854-15 · Cited by 35
Srivastava, S., Deshpande, D., Pasipanodya, J. G., et al. (2016) · Clinical Infectious Diseases · doi:10.1093/cid/ciw483 · Cited by 35
Yuan, Q., Song, G., Fullana–i–Palmer, P., et al. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.03.134 · Cited by 35
Tezzele, M., Salmoiraghi, F., Mola, A., et al. (2018) · Advanced Modeling and Simulation in Engineering Sciences · doi:10.1186/s40323-018-0118-3 · Cited by 35
“usually a uniform one |, and the so-called uncentered covariance matrix of the gradients of f(for a more deep understanding of these operators see for example [16]). Usually a Monte Carlo method (see [29]) is used in order to approximate the eigenpairs of this matrix (see [9]) as follows ˇ 1 NAS train NAS Xtrain i=1 r…”
Avdonin, A., Jaensch, S., Silva, C. F., et al. (2017) · Combustion and Flame · doi:10.1016/j.combustflame.2017.11.001 · Cited by 35
On Cryptographic Attacks Using Backdoors for SAT
Semenov, A., Заикин, О. С., Otpuschennikov, I. V., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12205 · Cited by 35
Njifon, I. C., Bertolus, M., Hayn, R., et al. (2018) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.8b01561 · Cited by 35
Zhang, X., Hansteen, F., Curtis, A., et al. (2019) · Journal of Geophysical Research Solid Earth · doi:10.1029/2019jb018552 · Cited by 35
“McMC is a class of methods which generate samples from a target120 probability density (Metropolis & Ulam, 1949; Hastings, 1970; Mosegaard & Tarantola,121 1995; Sivia, 1996; Malinverno et al., 2000; Malinverno, 2002; Malinverno & Briggs, 2004).122 In seismic tomography, a generalised McMC method…”
MethodYáñez, O., Inostroza, D., Úsuga-Acevedo, B., Vásquez‐Espinal, A., Pino‐Rios, R., Tabilo-Sepulveda, M., Garza, J., Barroso, J., Merino, G., & Tiznado, W. (2020) · Theoretical Chemistry Accounts · doi:10.1007/s00214-020-2548-5 · Cited by 36
El-Morshedy, M., Eliwa, M. S., & Tyagi, A. (2021) · Journal of Applied Statistics · doi:10.1080/02664763.2021.1928018 · Cited by 35
“Here, we utilize two important Markov Chain Monte Carlo (MCMC) approaches, known as Gibbs sampling [25] and Metropolis–Hastings (MH) algorithm [31,46] to generate the RSs from the posterior density and computing posterior”
MethodResource and Climate Implications of Landfill Mining
Frändegård, P., Krook, J., Svensson, N., et al. (2013) · Journal of Industrial Ecology · doi:10.1111/jiec.12039 · Cited by 35
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“Numerical approaches involving Markov chain Monte Carlo methods (Metropolis & Ulam 1949; Metropolis et al. 1953) are possible, but hardly feasible due to the huge parameter phase space.”
MethodRobust, Optimal Predictive Control of Jump Markov Linear Systems Using Particles
Blackmore, L., Bektassov, A., Ono, M., & Williams, B. C. (2007) · Springer eBooks · doi:10.1007/978-3-540-71493-4_11 · Cited by 34
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“4 Markov chain Monte Carlo We develop a Metropolis–within-Gibbs algorithm (Hastings, 1970; Metropolis and Ulam, 1949; Metropolis et al., 1953) where parameters are alternately sampled from their conditional distributions: the hyperparameters Hjh are sampled from a Gibbs sampler; the hierarchical parameters hjH;Y are s…”
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“This can be done using the Metropolis-Hastings algorithm [25,26].”
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“Thus, it is easily shown (e.g. Metropolis and Ulam 1949, Niederreiter 1992) that for a number of dimensions d > 4, the Monte Carlo integration method (in which the function evaluation points are taken at random) is more accurate (for the same total number of evaluation points) than classical…”
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“TheMC simulation method was proposed by Metropolis and Ulam (1949) and has been widely used to solve engineering problems such as blast fragmentation prediction (Morin and Ficarazzo 2006) and backbreak analysis (Sari, Ghasemi, and Ataei 2014).”
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“The statistical properties of the different designs were estimated from a large number of replications, i.e., we used Monte-Carlo simulation ( Metropolis and Ulam 1949 ) as a tool for our study.”
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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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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