This is a Bayesian modeling course and covers the following topics: (i) Meta-analysis and measurement models, (ii) Model evaluation and model selection, (iii). Multinomial processing trees and mixture models, (iv) Accumulator models and Drift-diffusion models.
Prerequisites: Knowledge of Bayesian inference basics (e.g., should be able to do Bayesian linear regressions)
- Kursleiter*in: Himanshu Yadav
ePortfolio: Nein