BIOSTAT 724
Introduction to Applied Bayesian Analysis
Lin Lin
Description from DukeHub
This is a first course in Bayesian statistical analysis for graduate students in biostatistics. The fundamentals of Bayesian inference are introduced, including Bayes' Theorem and prior and posterior distributions. Bayesian inference is compared and contrasted with frequentist methods through application to common problems in biostatistics. Inference based on conjugate families, as well as a computation-based introduction to Markov chain Monte Carlo methods is presented. Bayesian regression models are introduced, including model checking and selection, followed by an introduction to Bayesian hierarchical regression models. The course format emphasizes applied data analysis and is more heavily weighted toward heuristics and computation-based exploration of Bayesian methods rather than an intense mathematical treatment. Students should have a working knowledge of probability theory, likelihood, and applied frequentist data analysis including linear and logistic regression, and an understanding of how calculus is used in biostatistical applications. Prerequisite: Permission from the Director of Graduate Studies. Credits: 3
Skills
- data and statisticsdescription: "bayesian" and 15 more
AI-resilient: 0 of 5
- No: Physical, lab or field work
- No: Live work with people
- No: Judgment someone is accountable for
- No: A craft you practice with your hands or body
- No: Small, in-person format
The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works
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