BIOSTAT725

This course will teach students how to analyze biomedical data from a Bayesian inference perspective with a strong emphasis on using real-world data, including electronic health records, wearables, and imaging data. This is a second course in Bayesian inference and will start by introducing the hierarchical model as a flexible framework for analyzing complex data structures. This includes missing data, and spatial and longitudinal data. Then Bayesian machine learning approaches will be introduced, including regularization for high-dimensional data and scalable inference techniques for big data, including variational inference. Additional topics may be discussed from the Bayesian perspective, including causal inference, meta-analysis, and time-to-event data. While an applied course, the methods will be introduced from a mathematical perspective, allowing students to obtain a fundamental understanding of the introduced models. Students will learn computational skills for implementing Bayesian models using R and Stan. By the end of this course, students will be well-equipped to tackle complex problems in biomedical research using Bayesian inference. Pre-Req Biostat 724 or equivalent course with instructor permission

No Special Consent Required