Starting from March 2019, I’ve been teaching BayesCog to master students at the University of Vienna (weekly sessions). In 2020, due to the COVID-19 pandemic, the entire teaching was moved online – all teaching sessions were recorded and are publicly available on YouTube. See also the Tweet Thread on the summary of the course contents.
In October 2018, I led the 2nd edition of the BayesCog workshop, which included more content on model simulations and posterior predictive check, materials are available here.
In October 2016, I led a 2.5-day graduate level workshop on Bayesian Statistics and Bayesian Cognitive Modeling at UKE, Hamburg. A glimpse of the course description can be found here. A collection of the workshop’s topics includes but not limited to: R Programming, Bayes Theorem, MCMC Sampling, Binomial/Bernoulli Model, Linear Regression Model, 2-Armed Bandit Task and Reinforcement Learning Model, Hierarchical Modeling, Optimize Stan Codes, Model Comparison, Debug in Stan. Materials can be found here.
Together with Woo-Young Ahn and Nate Haines, we developed an R package hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers hierarchical Bayesian analysis of various computational models on an array of decision-making tasks. The latest version of the package and a step-by-step tutorial can be found here.
This is the teaching material from a 2-day workshop on the introduction to Matlab at the Institute of Psychology, Chinese Academy of Science. Please be aware that some of the notes inside are in Chinese.