As a computational social scientist, teaching students Bayesian statistics from an applied and computational perspective is essential. To this end, I have created an evolving set of labs in RStudio (Markdown) to help the Bayesian community better educate and prepare our students for (i) studying Bayesian statistics and (ii) understanding the importance of reproducibility in statistical analysis.
This short course consists of an introduction to RStudio and Markdown. We assume a knowledge of Bayesian statistics teaching students two basic yet foundational concepts. In the first lab, we concentrate on simulation, posterior sampling, and credible intervals. In the second lab, we focus on Gibbs sampling. We give simple labs, such that the material is at the level for undergraduate and graduate students. In turn, we hope that others will find such materials beneficial for teaching students applied Bayesian statistics.