Class material for "Bayesian Data Analysis & Cognitive Modeling" 2018
Project maintained by michael-franke
Hosted on GitHub Pages — Theme by mattgraham
Recommended literature on the use of R is R for Data Science, which is also available as a book.
Bayesian data analysis
To cover our basics, we will read selected chapters from John Kruschke’s Bayesian Data Analysis, 2nd edition of 2015.
- Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis (Vol. 2).
- The classic reference, but a bit more mathy.
- Richard McElreath (2016) Statistical Rethinking: A Bayesian Course with Examples in R and Stan.
- Very inspiring and illuminating big-picture narrative.
We will use selected chapters and exercises from Michael D. Lee and Eric-Jan Wagenmakers Bayesian modeling for cognitive science) from 2014. Code for this book is available for download for JAGS
- Stephan Lewandowsky & Simon Farrell (2011) Computational Modeling in Cognition: Principles and Practices.
- Great introduction to cognitive modeling in general with classic applications.