In this course we will simulate Bayesian models of cognition in the programming languages R and BUGS. We will work through the forthcoming book by Lee & Wagenmakers, with emphasis on the chapters regarding models of memory retention, signal detection theory, multidimensional scaling, take the best in decision making, psychometric models of number concepts, and the SIMPLE memory model.
No specific pre-requisites are demanded. However, this is a hands-on programming course using Bayesian methods, so it would be helpful to have some background knowledge of Bayesian models of mind or Bayesian data analysis, and programming experience. If you have previously taken P533 Bayesian Data Analysis, then you are set to hit the ground running, but that course is not required. The first few weeks of this course, P747, will be devoted to the basics of programming and Bayesian estimation.
The book and programs can be found (free) at http://www.ejwagenmakers.com/BayesCourse/BayesBook.html
Recommended textbook (for background preparation):
Kruschke, J. K. (2010). Doing Bayesian data analysis: A tutorial with R and BUGS.
Course format and grading:
The course will have some interactive lectures and demonstrations presented by the professor, but with many presented by students. Grades will be based on (a) presentation quality and (b) completion of hands-on exercises or simulations from the book.
This page is at URL = http://www.indiana.edu/~jkkteach/P747BayesModelsOfCogn/