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  Bayesian phylogenetic analysis of linguistic data using BEAST

Hoffmann, K., Bouckaert, R., Greenhill, S. J., & Kühnert, D. (2021). Bayesian phylogenetic analysis of linguistic data using BEAST. Journal of Language Evolution, lzab005. doi:10.1093/jole/lzab005.

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Hoffmann_Bayesian_JLangEvol_2021.pdf (Verlagsversion), 422KB
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 Urheber:
Hoffmann, Konstantin1, Autor           
Bouckaert, Remco, Autor
Greenhill, Simon J.2, Autor                 
Kühnert, Denise1, Autor                 
Affiliations:
1tide, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2591691              
2Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2074311              

Inhalt

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Schlagwörter: language evolution; historical linguistics; Bayesian methods; phylogenetics
 Zusammenfassung: Bayesian phylogenetic methods provide a set of tools to efficiently evaluate large linguistic datasets by reconstructing phylogenies—family trees—that represent the history of language families. These methods provide a powerful way to test hypotheses about prehistory, regarding the subgrouping, origins, expansion, and timing of the languages and their speakers. Through phylogenetics, we gain insights into the process of language evolution in general and into how fast individual features change in particular. This article introduces Bayesian phylogenetics as applied to languages. We describe substitution models for cognate evolution, molecular clock models for the evolutionary rate along the branches of a tree, and tree generating processes suitable for linguistic data. We explain how to find the best-suited model using path sampling or nested sampling. The theoretical background of these models is supplemented by a practical tutorial describing how to set up a Bayesian phylogenetic analysis using the software tool BEAST2.

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Sprache(n): eng - English
 Datum: 2021-09-23
 Publikationsstatus: Online veröffentlicht
 Seiten: 17
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: 1. Introduction
2. Bayesian phylogenetics
3. Models of evolution
4. Rate variation and calibration
5. Tree priors
6. Choosing the best analysis
7. Exploring the space of trees using BEAST2
8. Hypothesis testing with trees
9. Conclusion
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1093/jole/lzab005
 Art des Abschluß: -

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Titel: Journal of Language Evolution
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Oxford : Oxford University Press
Seiten: - Band / Heft: - Artikelnummer: lzab005 Start- / Endseite: - Identifikator: ISSN: 2058-458X
CoNE: https://pure.mpg.de/cone/journals/resource/journals