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On the genealogy of large populations
Abstract
A new Markov chain is introduced which can be used to describe the family relationships amongnindividuals drawn from a particular generation of a large haploid population. The properties of this process can be studied, simultaneously for all n, by coupling techniques. Recent results in neutral mutation theory are seen as consequences of the genealogy described by the chain.
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Cited by
Relaxed Phylogenetics and Dating with Confidence
Drummond, A. J., Ho, S. Y. W., Phillips, M. J., & Rambaut, A. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040088 · Cited by 6,463
“For example, the coalescent prior [56,57] is a commonly used prior for population-level data and has been extended to include various forms of demographic functions [58,59], sub-divided populations [60], and other complexities.”
BackgroundAncient DNA and the rewriting of human history: be sparing with Occam’s razor
Haber, M., Mezzavilla, M., Xue, Y., & Tyler-Smith, C. (2016) · Genome Biology · doi:10.1186/s13059-015-0866-z · Cited by 1,924
Bayesian species delimitation using multilocus sequence data
Yang, Z., & Rannala, B. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913022107 · Cited by 1,405
Distinguishing Migration From Isolation: A Markov Chain Monte Carlo Approach
Nielsen, R., & Wakeley, J. (2001) · Genetics · doi:10.1093/genetics/158.2.885 · Cited by 1,311
Markov chain Monte Carlo without likelihoods
Marjoram, P., Molitor, J., Plagnol, V., & Tavaré, S. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0306899100 · Cited by 1,199
Metadata
- Authors
- Kingmán, J. F. C.
- Venue
- Journal of Applied Probability
- Publisher
- Cambridge University Press
- Published
- 1982-01-01
- Volume
- 19
- Issue
- A
- Pages
- 27-43
- ISSN
- 0021-9002 (ISSN-L)
- OpenAlex
- W2319462502
- Cited by
- 1,628
- References
- 0
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kingmán, J. F. C. (1982). On the genealogy of large populations. Journal of Applied Probability. https://doi.org/10.1017/s0021900200034446
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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.
