Phylogenetic Methods and the Prehistory of LanguagesPeter Forster, Colin Renfrew McDonald Institute for Archaeological Research, 2006 - 198 Seiten Evolutionary ('phylogenetic') trees were first used to infer lost histories nearly two centuries ago by manuscript scholars reconstructing original texts. Today, computer methods are enabling phylogenetic trees to transform genetics, historical linguistics and even the archaeological study of artefact shapes and styles. But which phylogenetic methods are best suited to retracing the evolution of languages? And which types of language data are most informative about deep prehistory? In this book, leading specialists engage with these key questions. Essential reading for linguists, geneticists and archaeologists, these studies demonstrate how phylogenetic tools are illuminating previously intractable questions about language prehistory. This innovative volume arose from a conference of linguists, geneticists and archaeologists held at Cambridge in 2004. |
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Seite 162
... data language are absent from the data . We refer to cognate classes present in the data as data - cognates . We refer in contrast to those cognate classes which existed at some point in the collective history of the languages in this ...
... data language are absent from the data . We refer to cognate classes present in the data as data - cognates . We refer in contrast to those cognate classes which existed at some point in the collective history of the languages in this ...
Seite 164
... Cognate class 3 would not be represented in the data as it is present in just one observed language . subtree of the language - set tree . A simple illustration is given in Figure 14.2 . The languages at the leaves of the tree in Figure ...
... Cognate class 3 would not be represented in the data as it is present in just one observed language . subtree of the language - set tree . A simple illustration is given in Figure 14.2 . The languages at the leaves of the tree in Figure ...
Seite 166
... cognate data D ( or equivalently C ) . Let h ( x , gu , λ , OID ) denote the joint posterior proba- bility density for the unknown data - cognate birth times , x , and g , λ , μ and 0. The x are to some extent nuisance parameters . We ...
... cognate data D ( or equivalently C ) . Let h ( x , gu , λ , OID ) denote the joint posterior proba- bility density for the unknown data - cognate birth times , x , and g , λ , μ and 0. The x are to some extent nuisance parameters . We ...
Inhalt
CLARE J HOLDEN RUSSELL D GRAY | 19 |
Bantu Classification Bantu Trees and Phylogenetic Methods | 43 |
Chapter 6 | 67 |
Urheberrecht | |
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Albanian algorithms Anatolian Archaeological assumptions Bantu languages Bantu trees Bastin Bayesian binary Biology borrowing branch lengths Cambridge Chapter clade cladistics classification coded cognate cognate class cognate sets comparative computational correspondences data set data-cognate dating dialects distribution divergence Dyen East Bantu edge English estimates evidence evolutionary example Figure Forster genetic Germanic glottochronology Gray & Atkinson Greek guages Historical Linguistics Hittite Holden homoplasy Indo-European languages Indo-Iranian inference innovations islands language data language evolution language family lexical evolution lexical replacement lexicostatistics likelihood Malagasy Markov matrix maximum parsimony McDonald Institute McMahon meaning Molecular morphological Mycenaean Neighbor-Net Nichols nodes Pagel parameters phonetic phonological characters phylogenetic methods phylogenetic trees phylogeny posterior probability probability problem Proto-Indo-European rates of lexical reconstruction relationships Renfrew reticulations root semantic slot similar split splits graph statistical subgroup Swadesh Swadesh list telic tion Tocharian verbs vocabulary Warnow word lists zone