基于BEAST的语言数据贝叶斯系统发育分析

IF 2.1 0 LANGUAGE & LINGUISTICS
Konstantin Hoffmann, R. Bouckaert, Simon J. Greenhill, D. Kühnert
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引用次数: 10

摘要

贝叶斯系统发育方法提供了一套工具,通过重建代表语系历史的系统发育(家谱)来有效评估大型语言数据集。这些方法提供了一种强有力的方法来检验关于史前的假设,关于语言及其使用者的亚组、起源、扩展和时间安排。通过系统发育学,我们可以深入了解语言进化的一般过程,尤其是个体特征的变化速度。本文介绍了贝叶斯系统发育学在语言中的应用。我们描述了同源进化的替代模型,树分支进化率的分子时钟模型,以及适用于语言数据的树生成过程。我们解释了如何使用路径采样或嵌套采样来找到最适合的模型。这些模型的理论背景由一个实用教程补充,该教程描述了如何使用软件工具BEAST2建立贝叶斯系统发育分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian phylogenetic analysis of linguistic data using BEAST
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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来源期刊
Journal of Language Evolution
Journal of Language Evolution Social Sciences-Linguistics and Language
CiteScore
4.50
自引率
7.70%
发文量
8
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