The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Regenerative Analysis and Steady State Distributions for Markov Chains
Abstract
We apply regenerative theory to derive certain relations between steady state probabilities of a Markov chain. These relations are then used to develop a numerical algorithm to find these probabilities. The algorithm is a modification of the Gauss-Jordan method, in which all elements used in numerical computations are nonnegative; as a consequence, the algorithm is numerically stable.
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Cites
Kemeny, J. G., & Snell, J. L. (1976) · Medical Entomology and Zoology · Cited by 2,813
Isaacson, E., Keller, H. B., & Weiss, G. H. (1967) · Physics Today · doi:10.1063/1.3034415 · Cited by 2,262
Matrix-Geometric Solutions to Stochastic Models
Neuts, M. F. (1984) · Operations Research Proceedings · doi:10.1007/978-3-642-69546-9_91 · Cited by 1,660
Bush, K. A., Kemeny, J. G., & Snell, J. L. (1960) · The American Mathematical Monthly · doi:10.2307/2309264 · Cited by 1,546
Stochastic models in operations research
Heyman, D. P., & Sobel, M. J. (1982) · Medical Entomology and Zoology · Cited by 1,041
Self-citation
Cited by
Stochastic Complementation, Uncoupling Markov Chains, and the Theory of Nearly Reducible Systems
Meyer, C. D. (1989) · SIAM Review · doi:10.1137/1031050 · Cited by 316
Metadata
- Authors
- Grassmann, W. K., Taksar, M. I., & Heyman, D. P.
- Venue
- Operations Research
- Published
- 1985-10
- Volume
- 33
- Issue
- 5
- Pages
- 1107-1116
- ISSN
- 0030-364X (ISSN-L)
- 1526-5463
- OpenAlex
- W1988914905
- Cited by
- 364
- References
- 5
- FWCI
- 4.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Grassmann, W. K., Taksar, M. I., & Heyman, D. P. (1985). Regenerative Analysis and Steady State Distributions for Markov Chains. Operations Research. https://doi.org/10.1287/opre.33.5.1107
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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.
