A test of Generalized Bayesian dating: A new linguistic dating method

Abstract

In current practice, when dating the root of a Bayesian language phylogeny the researcher is required to supply some of the information beforehand, including a distribution of root ages and dates for some nodes serving as calibration points. In addition to the potential subjectivity that this leaves room for, the problem arises that for many of the language families of the world there are no available internal calibration points. Here we address the following questions: Can a new Bayesian framework which overcomes these problems be introduced and how well does it perform? The new framework that we present is generalized in the sense that no family-specific priors or calibration points are needed. We moreover introduce a way to overcome another potential source of subjectivity in Bayesian tree inference as commonly practiced, namely that of manual cognate identification; instead, we apply an automated approach. Dates are obtained by fitting a Gamma regression model to tree lengths and known time depths for 30 phylogenetically independent calibration points. This model is used to predict the time depths of both the root and the internal nodes for 116 language families, producing a total of 1,287 dates for families and subgroups. It turns out that results are similar to those of published Bayesian studies of individual language families. The performance of the method is compared to automated glottochronology, which is an update of the classical method of Swadesh drawing upon automated cognate recognition and a new formula for deriving a time depth from percentages of shared cognates. It is also compared to a third dating method, that of the Automated Similarity Judgment Program (ASJP). In terms of errors and correlations with known dates, ASJP works better than the new method and both work better than automated glottochronology.

Strategies in tracing linguistic variation in a corpus of Old Irish texts (CorPH)

“…The use of Bayesian methods has seen an upsurge in historical linguistics in recent years. The most common application is for establishing phylo-genetic relationships within language families and for dating the bifurcations in language trees (see the recent examples of Rama & Wichmann, 2020, or Sagart et al, 2019. The use of Bayesian methods to date linguistic developments within a historically attested language has so far only been attempted by Hellwig (2019) and ( 2020) for Vedic Sanskrit, independently from and parallel to the work in ChronHib.…”

Testing methods of linguistic homeland detection using synthetic data

“…The phylogenetic inference model also has a relaxed branch rates model where the branch rates are drawn from a uncorrelated lognormal distribution [43]. 3 The phylogeographic model is a relaxation of the Brownian motion model where the likelihood of latitude and longitude drawn from independent uniform distributions are estimated using a bivariate normal distribution. The parameters of the variance matrix of the bivariate normal distribution are scaled by a lognormally distributed scaler for each branch leading to a separate variance matrix for each branch.…”

“…The prior probability that there is a rate shift is given as the expected number of rate shifts (5) divided by the number of branches (20 -1 = 19). The rate shift multiplier being 1 (i.e., no rate shift) or not 1 (drawn from 3 The XML file was crafted based on the tutorial at https://taming-the-beast.org/tutorials/LanguagePhylogenies/.…”

“…Language group origins may also, for instance, form the backbone for a study relating to linguistic typology or prehistorical areal linguistics [2]. When combined with information on dates for language groups [3], inferences about homelands become particularly powerful as contributions to world prehistory.…”

Testing methods of linguistic homeland detection using synthetic data

“…The phylogenetic inference model also has a relaxed branch rates model where the branch rates are drawn from a uncorrelated lognormal distribution [36]. 3 The phylogeographic model is a relaxation of the Brownian motion model where the likelihood of latitude and longitude drawn from independent uniform distributions are estimated using a bivariate normal distribution. The parameters of the variance matrix of the bivariate normal distribution are scaled by a lognormally distributed scaler for each branch leading to a separate variance matrix for each branch.…”