Shorter, online resources:
- The Bayesian flip: Correcting the Prosecutor’s fallacy by Skorupski and Wainer – a very short conceptual introduction to Bayesian methods
- Bayesian Basics– by Michael Clark– an introduction to Bayesian models in R (57 pages)
BayesLMMTutorial Online materials to support the Sorensen and Vasishth bayesian tutorial paper.(contains a link to the paper)
Rstan website -RStan is the program we use to implement these models in R.
rstan/wiki/RStan-Getting- Started — RStan quickstart guide
- http://jakewestfall.org/misc/SorensenEtAl.pdf — tutorial for mixed effects models with STAN
- Kruschke, John. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press, 2014.—very accessible,written from an experimental psychology perspective.
- Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. Bayesian Data Analysis. CRC Press, 2013.—written from a statistical (and computational) perspective, but has many important discussion about prior selection (especially for non-experimental data). It is also important to understand what statisticians (and computational folks focused on data analysis algorithms) are concerned with when designing these techniques.
- Gelman, Andrew, and Jennifer Hill. Data analysis using regression and multilevel/hierarchical models.
- For an overview of why forced aligners are great look at my slides from my talk on them
- To get textgrids, try any one of these forced aligners: p2fa, SPPAS, FAVE-align
- For help setting up the p2fa on a mac, try linguisticmystic
- For acoustic measurements of files for which you have textgrids, try ProsodyPro
- Mietta’s Praat Scripts (Now called Speech CT, the Speech Corpus Toolkit)
- Shigeto Kawahara’s praat resources: http://user.keio.ac.jp/~kawahara/resource.html