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Together with Peter König. In this course we watched the mini-lectures of Jarad Niemi (can be found on youtube) and discussed the content. This allows students to rewatch the videos, introduce topics themselves and give them multiple different angles of the same topic. On the negative side, the videos are not always motivating the content very well. Every week we had homeworks in R, which we discussed intensively in the course. Some of the exercises are taken from “Doing Bayesian Data Analysis” and “Statistical Rethinking” which are both books that I highly recommend. Most are made by myself. Feel free to use them under CC-BY.
Week 1 Probabilities & Bayes Rule
Week 2 Bernoulli, Iterative Updating
Week 3 Monte Carlo Integration implementation
Week 4 Inverse Cumulative Function and Accept-Reject Method implementation
Week 5 Metropolis Implementation and Introduction to STAN
Week 6 Multi-Parameter Metropolis and Metropolis-within-Gibbs implementation. Bonus:Banana-Shaped Posterior
Week 7 Hierarchical Models in STAN
Week 8 Poisson distribution in STAN and posterior predictive model checks (data file .csv)