Bayesian Inference

Teacher: Eric-Jan Wagenmakers

Content: This course will cover the theory and practice of “common sense expressed in numbers”, that is, Bayesian inference. In the first part of the course I
will use the binomial model to cover the theoretical building blocks (e.g., prior and posterior distributions, coherence, parameter estimation and Bayes factor hypothesis testing, vague vs. informed prior distributions, model-averaging, model misspecification, etc.). In the second part I will showcase Bayesian inference in practice and feature Bayesian t-tests, regression, ANOVA, and other models.