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Book Chapter

Bayesian Phylolinguistic

MPS-Authors
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Greenhill,  Simon J.
Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society;

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Heggarty,  Paul
Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society;

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Gray,  Russell D.
Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society;

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Citation

Greenhill, S. J., Heggarty, P., & Gray, R. D. (2020). Bayesian Phylolinguistic. In R. D. Janda, B. D. Joseph, & B. S. Vance (Eds.), The Handbook of Historical Linguistics [II] (pp. 226-253). Hoboken, New Jersey: Wiley-Blackwell.


Cite as: http://hdl.handle.net/21.11116/0000-0006-F6B0-D
Abstract
Change is coming to historical linguistics. Big, or at least “bigish data” (Gray and Watts 2017), are now becoming increasingly available in the form of large web accessible lexical, typological and phonological databases (e.g. ABVD (Greenhill et al 2008), Chirilla (Bowern 2016), Phoible (Moran 2014), WALS (Haspelmath 2014), Autotyp (Bickel et al 2017) and the soon to be released Lexibank, Grambank, Parabank and Numeralbank http://www.shh.mpg.de/180672/glottobank). This deluge of data is way beyond the ability of any one person to process accurately in their head. The deluge will thus inevitably drive the demand for appropriate computational tools to process and analyze the fast wealth of freely available linguistic information. In this chapter we will briefly describe one such set of computational tools – Bayesian phylogenetic methods – and outline their utility for historical linguistics. We will focus on four main questions: what is Bayesian phylolinguistics, why does this approach typically focus on lexical data, how is it able to estimate divergence dates, and how reliable are the results?