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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 (Publisher version), 422KB
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Hoffmann_Bayesian_JLangEvol_2021.pdf
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 Creators:
Hoffmann, Konstantin1, Author           
Bouckaert, Remco, Author
Greenhill, Simon J.2, Author                 
Kühnert, Denise1, Author                 
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              

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Free keywords: language evolution; historical linguistics; Bayesian methods; phylogenetics
 Abstract: 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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Language(s): eng - English
 Dates: 2021-09-23
 Publication Status: Published online
 Pages: 17
 Publishing info: -
 Table of Contents: 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
 Rev. Type: Peer
 Identifiers: DOI: 10.1093/jole/lzab005
 Degree: -

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Title: Journal of Language Evolution
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: - Sequence Number: lzab005 Start / End Page: - Identifier: ISSN: 2058-458X
CoNE: https://pure.mpg.de/cone/journals/resource/journals