Bayesian statistics has been applied to a language change modeling (ex Niyogi & Berwick 1995, Griffiths & Kalish 2007 etc). I am looking, however, for any research that implemented Bayesian statistics to actual linguistic data, ex. a diachronic or synchronic linguistic variation. I would greatly appreciate any suggestion or advice, as I am hoping to use (hierarchical-)Bayesian model on historical categorical data in my thesis. Thank you!
The work on linguisitic phylogenies ought to be mentioned:
Greenhill, S. J., & Gray, R. D. (2009). Austronesian language phylogenies: myths and misconceptions about Bayesian computational methods. In : Alexander Adelaar and Andrew Pawley, eds. Austronesian historical linguistics and culture history: a festschrift for Robert Blust, 375-397.
See refs there for more.
--John Nerbonne
http://language.psy.auckland.ac.nz/files/greenhill_and_gray2009/Greenhill_and_Gray2009.pdf
Hej,
within
Analyzing Linguistic Data: A Practical Introduction to Statistics using R Paperback – March 17, 2008
by R. H. Baayen (Author)
you will find first steps analysing your data with R using GLMM in combination with MCMC and literatur to go further..
Sven
Dear all,
My data span over several centuries and as it happens in many diachronic studies there are chronological gaps. In my model so far (I am using jags to run MCMC chain) I am using logistic regression, where all my exploratory variables, including century, are categorical. I have assigned a separate prior to each coefficient, as a dnorm( 0 , 1.0E-12 ) - (I am not sure yet if it is the best non-informative prior). But I could also have all parameters mutually inform each other, instead of separate estimates. Which model would/could be a better fit and what prior might be the best one to choose? Any suggestions are greatly appreciated!
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