160161 PS Bayesian Approaches to Cognitive Modelling (2022S)

In this proseminar, methodological basics for dealing with empirical data as well as an insight into statistical modelling are taught. In this course, students deal with statistical models (linear regression, hierarchical models, etc.), learn how to programme them and use them for data analysis. The open-source statistical software R is used for this purpose. Programming knowledge is not a prerequisite, but will help with the tasks. We will start with the basics of statistics (descriptive statistics, distributions, probability, assumptions, basics of inferential statistics, linear regression, etc.), and then work on the basics of Bayesian statistics/modeling, as this offers a wide range of applications for cognitive scientists and linguists and is a valuable tool. Furthermore, we will learn what a cognitive model is and how we can use it for our research. The aim of this seminar is to consolidate the students' existing knowledge of statistics and to add new perspectives. In addition, the programming exercises will practice the practical use of the programming language R and the probabilistic language STAN. The units usually consist of an input part by the seminar leader and a practical part in which the students work on exercises.