Applied Bayesian Statistics

Graduate / Professional

Students learn to analyze data using Bayesian statistical methods. Topics include: the difference between the frequentist and Bayesian approaches to statistics; basic theory of Bayesian estimation (prior and posterior distributions, Bayes’ Rule, etc.); hypothesis testing using Bayes factors and posterior credible intervals; and Bayesian versions of commonly used statistical tests and models, such as the t-test, linear regression etc. The course emphasizes applications ( how to do the analysis) rather than mathematical details. However, students learn some basic theory in order to understand what they are doing. Prerequisites: Students are expected to have taken a basic statistics course such as PSYC 518. We use R (a programming language specialized for statistical analysis), so students should already either be familiar with R or be able to pick it up very quickly (e.g. if they're already very comfortable with Python or Matlab). T 10:30am-11:50am