As an introductory Bayesian statistics course, key concepts in conditional probability will be discussed at first. The topics expand to change of measure, Bayesian inference in both small and large samples, multi-parameter models, along with sequential approaches in Bayesian data analysis. Starting from Hierarchical models, sensitivity, randomization, and connections in Bayesian will also be discussed.

Students are expected to train a critical view on frequentist’s statistics in the context of Bayesian.