Two topics in Bayesian will be discussed. Following Introduction to Bayesian Statistics, this course covers Bayesian regression models from single variable linear model to a variety of multi-variable generalized models. Models that are non-linear still can be built by Bayesian approach, which will later become a part of Factor Analysis in the field of Machine Learning.

The course also deals with handy Bayesian tools for missing data models, multi-stage decision making w/ updating information set, and 1 vs. many people decision analysis, all of which are logical extension of Bayesian regression modeling.