This course will teach students to analyze biomedical data using Bayesian inference, focusing on real-world data like electronic health records, wearables, and imaging. It covers hierarchical models for complex data, including missing, spatial, and longitudinal data. Students will also learn Bayesian machine learning techniques, such as regularization for high-dimensional data and scalable inference methods like variational inference. Additional topics may include causal inference, meta-analysis, and time-to-event data. The course combines mathematical theory with practical skills in R and Stan, preparing students to address complex biomedical research problems using Bayesian methods.
