BIOSTAT 725 - Bayesian Health Data Science

Spring 2026 - Prof. Sam Berchuck

Overview

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. The course will begin by introducing the machinery of Bayesian statistics through the lens of linear regression, giving enough context for students with no prior experience with Bayesian statistics. A history of computational approaches used in Bayesian statistics will be given before ultimately landing on Stan, a state-of-the-art probabilistic programming language that makes Bayesian inference accessible as a viable data science tool. The course will then branch out from regression and introduce Bayesian versions of machine learning tools, including regularization and classification. The course will then emphasize Bayesian hierarchical models, including Gaussian process models for temporal and spatial data; and clustering. Additional topics may be discussed from the Bayesian perspective, including causal inference, and meta-analysis. 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-requisites

BIOSTAT 724 (Introduction to Applied Bayesian Analysis) or equivalent course with instructor permission. Interested students with different backgrounds should seek instructor consent.

Class meetings

Lecture

Tue & Thu 11:45am - 1pm

Hock 10089

Teaching team

Instructor

Prof. Sam Berchuck is an Assistant Professor in the Department of Biostatistics & Bioinformatics and Statistical Science at Duke University, and a faculty affiliate of Duke AI Health. His work focuses on applications of Bayesian methods in biomedical research settings, most prominently for clinical decision support tools using electronic health records.

Office hours	Location
Tue 2 - 4pm	Hock 10092
or by appointment.

Teaching assistants

Name	Role	Office Hours	Location
Dr. Youngsoo Baek	TA	Wed 2 - 4pm	Hock 10090
Braden Scherting	TA	Mon 9 – 10am Wed 1 – 2pm	Old Chemistry 203A/B

License

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