The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Why the Monte Carlo method is so important today
Open accessAbstract
Since the beginning of electronic computing, people have been interested in carrying out random experiments on a computer. Such Monte Carlo techniques are now an essential ingredient in many quantitative investigations. Why is the Monte Carlo method (MCM) so important today? This article explores the reasons why the MCM has evolved from a ‘last resort’ solution to a leading methodology that permeates much of contemporary science, finance, and engineering. WIREs Comput Stat 2014, 6:386–392. doi: 10.1002/wics.1314 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical Models > Simulation Models
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Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · The Journal of Physical Chemistry · doi:10.1021/j100540a008 · Cited by 10,136
Simulation Modeling and Analysis.
Schervish, M. J., Law, A. M., & Kelton, W. D. (1983) · Journal of the American Statistical Association · doi:10.2307/2288169 · Cited by 8,697
Markov Chain Monte Carlo in Practice
(1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,757
The cosmological simulation code gadget-2
Springel, V. (2005) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2005.09655.x · Cited by 6,368
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343
Gillespie, D. T. (1976) · Journal of Computational Physics · doi:10.1016/0021-9991(76)90041-3 · Cited by 6,029
Motwani, R., & Raghavan, P. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814075 · Cited by 5,123
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Hansen, B. B., Spittle, S., Chen, B., Poe, D., Zhang, Y., Klein, J. M., Horton, A., Adhikari, L., Zelovich, T., Doherty, B. W., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00385 · Cited by 3,422
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Alamia, A., Larsson, A., Breitholtz, C., & Thunman, H. (2017) · International Journal of Energy Research · doi:10.1002/er.3758 · Cited by 105
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Metadata
- Authors
- Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. I.
- Venue
- WIREs Computational Statistics
- Publisher
- Wiley
- Published
- 2014-06-20
- Volume
- 6
- Issue
- 6
- Pages
- 386-392
- ISSN
- 1939-0068 (ISSN-L)
- 1939-5108
- OpenAlex
- W2162602080
- Cited by
- 757
- References
- 58
- FWCI
- 22.68
- Type
- journal article
- Language
- en
- Open access
- green
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