The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions
Abstract
We consider the Monte Carlo problem of generating points uniformly distributed within an arbitrary bounded (measurable) region. The class of Markovian methods considered generate points asymptotically uniformly distributed within the region. Computational experience suggests the methods are potentially superior to conventional rejection techniques for large dimensional regions.
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Cites
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Doob, J. L., & Doob, J. L. (1953) · CERN Document Server (European Organization for Nuclear Research) · Cited by 9,053
Hammersley, J. M., & Handscomb, D. C. (1964) · Springer eBooks · doi:10.1007/978-94-009-5819-7 · Cited by 4,054
Ross, S. M. (1982) · Cited by 3,812
Simulation and the Monte Carlo Method
Rubinstein, R. Y., & Kroese, D. P. (2016) · Wiley Series in Probability and Statistics · doi:10.1002/9781118631980 · Cited by 3,356
A Course in the Geometry of n-Dimensions.
Draper, J., & Kendall, M. G. (1962) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2986960 · Cited by 142
Generating random vectors uniformly distributed inside and on the surface of different regions
Rubinstein, R. Y. (1982) · European Journal of Operational Research · doi:10.1016/0377-2217(82)90161-8 · Cited by 87
A Course in the Geometry of n Dimensions
Lancaster, H. O., & Kendall, M. G. (1963) · Revue de l Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1401381 · Cited by 86
Random Variate Generation: A Survey.
Schmeiser, B. W. (1980) · NCSU Libraries Repository (North Carolina State University Libraries) · Cited by 49
Cited by
Derivative-free optimization: a review of algorithms and comparison of software implementations
Rios, L. M., & Sahinidis, N. V. (2013) · Journal of Global Optimization · doi:10.1007/s10898-012-9951-y · Cited by 1,276
Global organization of metabolic fluxes in the bacterium Escherichia coli
Almaas, E., Kovács, B., Vicsek, T., Oltvai, Z. N., & Barabási, A.-L. (2004) · Nature · doi:10.1038/nature02289 · Cited by 709
The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
Campi, M. C., & Garatti, S. (2008) · SIAM Journal on Optimization · doi:10.1137/07069821x · Cited by 560
Random walks in a convex body and an improved volume algorithm
Lovász, L., & Simonovits, M. (1993) · Random Structures & Algorithms · doi:10.1002/rsa.3240040402 · Cited by 409
Metadata
- Authors
- Smith, R. L.
- Venue
- Operations Research
- Published
- 1984-12
- Volume
- 32
- Issue
- 6
- Pages
- 1296-1308
- ISSN
- 0030-364X (ISSN-L)
- 1526-5463
- OpenAlex
- W2125299871
- Cited by
- 531
- References
- 9
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Smith, R. L. (1984). Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions. Operations Research. https://doi.org/10.1287/opre.32.6.1296
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
