The DOI matches its Crossref record.
A Bayesian approach to inference about a change-point in a sequence of random variables
Abstract
A Bayesian approach is considered to the problem of making inferences about the point in a sequence of random variables at which the underlying distribution changes. Inferences are based on the posterior probabilities of the possible change-points. Detailed analyses are given for cases in which the distributions are binomial and normal, and numerical illustrations are provided. An informal sequential procedure is also noted.
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Citations
Cited by
Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance
Inclán, C., & Tiao, G. C. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476824 · Cited by 1,156
Bayesian Online Changepoint Detection
Adams, R. P., & MacKay, D. J. C. (2007) · arXiv · doi:10.48550/arxiv.0710.3742 · Cited by 601
Scargle, J. D. (1998) · The Astrophysical Journal · doi:10.1086/306064 · Cited by 398
Control Charts Based on Attribute Data: Bibliography and Review
Woodall, W. H. (1997) · Journal of Quality Technology · doi:10.1080/00224065.1997.11979748 · Cited by 266
Merli, M. G., & Raftery, A. E. (2000) · Demography · doi:10.2307/2648100 · Cited by 110
Metadata
- Authors
- SMITH, A. F. M.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1975-08-01
- Volume
- 62
- Issue
- 2
- Pages
- 407-416
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W1966042408
- Cited by
- 320
- References
- 0
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A Bayesian approach to inference about a change-point in a sequence of random variables. https://app.cochat.ai/papers/10.1093/biomet/62.2.407
- The paper
- SMITH, A. F. M. (1975). A Bayesian approach to inference about a change-point in a sequence of random variables. Biometrika. https://doi.org/10.1093/biomet/62.2.407
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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.
