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Abstract and Figures

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?
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BAYESIAN PHYLOLINGUISTICS
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1. INTRODUCTION
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2. WHAT IS BAYESIAN PHYLOGENETICS?
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3.1 The Lexicon for Establishing Relatedness?
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C2-"826+:&6,+&*+:3-6:&$>&2%2-7:+:&$>&:"#3-26+8&8262&Q,+*+&6,+&@$#=-+6+&,":6$*7&":&F%$Q%(&
5.1 Verification with the comparative method.
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6,$:+& $>& 6,+& @$#=2*26"C+& #+6,$8(& !638"+:& :,$Q"%5& :6*$%5& @$%@$*82%@+& Q"6,& 6,+& @$#=2*26"C+&
#+6,$8& 2%8& 6,+"*& =,7-$5+%+6"@2--7& +:6"#26+8& :3O5*$3=:& "%@-38+& L:-"2%& V;3%%& +6& 2-(& 0H..X/&
L6,2O2:F2%& V!"@$-"& 2%8& 4$-6$%& 0H.JX/& S2%63& V)*$--+#3%8& +6& 2-(& 0H.Kj& )*$--+#3%8& 0H.0X/&
A,2=2@3*2%&VS"*@,2--/&;3%%/&2%8&)*++%,"--&0H.[X/&43$%&1+%"%:3-2&V)*++%,"--&0H.KX/&G%8$fB3*$=+2%&
VS$3@F2+*6& +6& 2-(& 0H.0j& A,2%5&+6&2-(& 0H.KX/&12#2fM73%52%& VS$Q+*%& 2%8&L6F"%:$%&0H.0X/&!+#"6"@&
13
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P3="f)32*2%"&VD"@,2+-&+6&2-(&0H.KX/&2%8&N*2-"@&V!7*c•%+%&+6&2-(&0H.<X(&
P$&826+&=+*,2=:&6,+&O+:6&@$#=2*":$%&O+6Q++%&6,+&>"%8"%5:&$>&6,+&6*28"6"$%2-&@$#=2*26"C+&#+6,$8&
2%8& =,7-$5+%+6"@& #+6,$8:& @2%& O+& >$3%8& "%& 6,+& L3:6*$%+:"2%& -2%5325+:(&P,+*+& 2*+& :6*$%5&
,7=$6,+:+:& O2:+8& 2O$36& 6,+& $*"5"%/& 2%8& :3O5*$3="%5& $>& 6,+:+& -2%5325+:& 8+*"C+8& >*$#& 6,+&
@$#=2*26"C+& #+6,$8/& 2%8& *$O3:6& 826+:& >$*& #2%7& $>& 6,+& #2"%& 5*$3=:& 8+*"C+8& >*$#& 2*@,2+$-$57&
V;"2#$%8&2%8&S+--Q$$8&0HH<j&S+--Q$$8&2%8&;"^$%&0HHZj&9$O+*6&S-3:6&0H.<j&L%8*+Q&12Q-+7&0HH0j&
)*++%&0HH<j&L(&D(&!(&p$&+6&2-(&0H.JX(&P,+&L3:6*$%+:"2%&-2%5325+:&$*"5"%26+8&"%&P2"Q2%&Q,+*+&d&$>&
6,+&.H&#2c$*&O*2%@,+:&2*+&:6"--&:=$F+%&V9$O+*6&S-3:6&.dddj&9$O+*6&S-3:6&0H.<X&2*$3%8&KKHH&7+2*:&
25$&V9$O+*6&S-3:6&0H.<X(&P,+&*+#2"%"%5&O*2%@,&_&1*$6$fD2-27$f1$-7%+:"2%&_&:=*+28&:$36,&6,*$35,&
6,+& 1,"-"=="%+:& 2*$3%8& .HHH& 7+2*:& -26+*& VS+--Q$$8& 2%8& ;"^$%& 0HHZj& 9$O+*6& S-3:6& .dddj& L%8*+Q&
12Q-+7&0HH0X/&6,*$35,&G%8$%+:"2&2%8&2-$%5&6,+&@$2:6&$>&M+Q&)3"%+2(&S7&2*$3%8&<HHHf<0HH&7+2*:&
25$&6,+&L3:6*$%+:"2%:&@2%&O+&:6*$%5-7&-"%F+8&6$&6,+&8+C+-$=#+%6&$>&6,+&?2="62&@3-63*2-&@$#=-+E&"%&
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:62#=+8& 2%8& *+8f:-"==+8& =$66+*7& :67-+/& 2& *2%5+& $>& 8$#+:6"@26+:/& 2%8& :$@"2-& =*2@6"@+:(& b"%2--7/&
2*$3%8&0ZHHf<HHH&7+2*:&25$&6,+&L3:6*$%+:"2%:&:+66-+8&W+:6+*%&1$-7%+:"2&2*$3%8&0dHH&7+2*:&25$/&
=23:+8&252"%&>$*&.HHH&7+2*:/&2%8&6,+%&+%6+*+8&B2:6&1$-7%+:"2&VW"-#:,3*:6&+6&2-(&0H..X(&
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-2%5325+:& 6,26& $*"5"%26+8& >*$#& 2& *2="8& =$=3-26"$%& +E=2%:"$%& "%6$& 2%8& 2@*$::& 2& *2%5+& $>& %+Q&
+%C"*$%#+%6:/& +%@$3%6+*"%5& :=+2F+*:& $>& C+*7& 8">>+*+%6& -2%5325+:/& 2%8& 3%8+*5$"%5& :3O:62%6"2-&
:,">6:&"%&=$=3-26"$%&:"^+(&L--&$>&6,+:+&>2@6$*:& ,2C+&*+:3-6+8&"%& ,35+&C2*"26"$%&"%&@$5%26+&*+6+%6"$%&
*26+:& O+6Q++%& -2%5325+:& V9$O+*6&S-3:6& 0HHHj&)*++%,"--&0H.KX(& h%+&$>&6,+& -2*5+:6& -+E"@$:626":6"@2-&
:638"+:&+C+*&@$%83@6+8&V;7+%&.d[0X&$%&6,+:+&-2%5325+:&=*$83@+8&O"^2**+&*+:3-6:&_&@$%@-38"%5&6,26&
6,+& ,$#+-2%8& $>& 6,+& L3:6*$%+:"2%& -2%5325+& >2#"-7& Q2:& "%& M+2*& h@+2%"2& *26,+*& 6,2%& "%& P2"Q2%&
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6,+&T!-$Q&S$26U/&:355+:6:&6,+&L3:6*$%+:"2%:&$*"5"%26+8& "%& W2--2@+2& V6,+& *+5"$%&2*$3%8&#$8+*%&
827&!3-2Q+:"X&O+6Q++%&.</HHH&2%8& .I/HHH& 7+2*:&25$&O+>$*+&:=*+28"%5&%$*6,&"%6$&6,+& 1,"-"=="%+:&
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14
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5.2 Validation with simulations
P,+&:+@$%8&Q27&Q+& #"5,6&C2-"826+& =,7-$5+%+6"@&"%>+*+%@+&":&6$&:"#3-26+&8262&$%&2&F%$Q%&k6*3+l&
=,7-$5+%7&2%8&6,+%&2%2-7^+&6,+&:"#3-26+8&8262&6$&:++&">&Q+&*+@$C+*&6,+&k6*3+l&6*++(&P,":&2==*$2@,&
":& @$##$%-7& 3:+8& "%& 6,+& O"$-$5"@2-& =,7-$5+%+6"@:& -"6+*263*+& 6$& 6+:6& 6,+& +>>+@6"C+%+::& $>& 6,+:+&
#+6,$8:(& )*++%,"--& +6& 2-& V)*++%,"--/& A3**"+/& 2%8& )*27& 0HHdX& 2==-"+8& 6,":& -$5"@& 6$& 6+:6& ,$Q& Q+--&
S27+:"2%& =,7-$5+%+6"@& #+6,$8:& @$3-8& *+@$C+*& 6,+& 6*3+& 6*++& Q"6,& -+E"@2-& @$5%26+& 8262(& b"*:6/& Q+&
@*+26+8& 6Q$& 6*++& 6$=$-$5"+:\&$%+&:,2=+8& -"F+&1$-7%+:"2%& Q"6,& 2%& 3%O2-2%@+8/&@,2"%+8& 6$=$-$57/&
2%8& 6,+& $6,+*& :,2=+8& -"F+& N6$fL^6+@2%& Q"6,& 2& Q+--fO2-2%@+8& 6*++(& P,+%& Q+& 3:+8& 2& :"#=-+&
k:6$@,2:6"@&;$--$l&#$8+-&$>&-"%53":6"@&+C$-36"$%&VM"@,$--:&2%8&)*27&0HH[j&L6F"%:$%&+6&2-(&0HHKX&6$&
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15
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Conclusion
P,+&C"+Q&Q+&,2C+&62F+%&,+*+&":&6,26&S27+:"2%&#+6,$8:&2*+&2&=$Q+*>3-&supplement&6$&6*28"6"$%2-&
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8"C+*5+%@+&826+:(&G%&6,+&>363*+&Q+&+E=+@6&6$&:++&#$*+&@$#O"%+8&2%2-7:+:&$>&-+E"@2-/&=,$%$-$5"@2-&
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-+E"@$:626":6"@:&=2:6/&6,+%&6,+*+&":&2%&+E@"6"%5&>363*+&6$&+#O*2@+(&
&
16
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Conference on Austronesian Linguistics: Proceedings, Fascicle I, Western Austronesian/&
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Linguistics/&+8"6+8&O7&R+"6$3%&B(&2%8&1&'fp(&?"/&<._dJ(&P2"=+"/& P2"Q2%\&!7#=$:"3#&!+*"+:&$>&
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S$3@F2+*6/& 9(9(& 0H.H(&;+%:"P*++\&#2F"%5& :+%:+& $>& :+6:& $>& =,7-$5+%+6"@& 6*++:(& Bioinformatics(&
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S$3@F2+*6/&9+#@$&9(/&1(&?+#+7/&D"@,2+-&;3%%/&!"#$%&'(&)*++%,"--/&L-+E2%8+*&Y(&L-+F:+7+%F$/&2(&'(&
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Nukuoro
Luangiua
Rennellese
Samoan
Tongan
FijianBau
Rotuman
25
&
&
Eastern Polynesian
Ellicean
Futunic
Micronesian
North&Central Vanuatu
SouthVanuatu
S.E. Solomonic
SouthVanuatu
Temotu
Admiralties
Meso-Melanesian
NorthNewGuinea
PapuanTip
SHWNG
Central Malayo-Polynesian
Western Malayo-Polynesian
Philippines
Formosan
PCP
Oc WOc
MP
CEMP
EMP
PCP
Oc
EMP
CEMP
MP
Meso-Melanesian
Admiralties
Temotu
S.E. Solomonic
Ethnologue Phylogenetic
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Bayesian methods have become very popular in molecular phylogenetics due to the availability of user-friendly software for running sophisticated models of evolution. However, Bayesian phylogenetic models are complex, and analyses are often carried out using default settings, which may not be appropriate. Here we summarize the major features of Bayesian phylogenetic inference and discuss Bayesian computation using Markov chain Monte Carlo (MCMC) sampling, the diagnosis of an MCMC run, and ways of summarizing the MCMC sample. We discuss the specification of the prior, the choice of the substitution model and partitioning of the data. Finally, we provide a list of common Bayesian phylogenetic software packages and recommend appropriate applications.
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McMahon, A. M., & McMahon, R. (2006). Why linguists don?t do dates. In P. Forster, & C. Renfrew (Eds.), Phylogenetic methods and the prehistory of languages. (pp. 153-160). Cambridge: McDonald Institute for Archaeological Research.