Bayesian Statistical Modeling and Data Analysis

Description from DukeHub

Principles of data analysis and advanced statistical modeling. Bayesian inference, prior and posterior distributions. Linear, multivariate, and multi-level models. Model checking and selection. Stochastic simulation by Markov Chain Monte Carlo. Prerequisites: Regression (STA 221L) and Statistical Inference (STA 332). Not open to students with credit for STA 360L, STA 602L, or STA 702L.

Skills

• data and statisticsdescription: "data analysis" and 6 more
• lab workhas a lab component

AI-resilient: 1 of 5 1/5

• ✓Yes: Physical, lab or field work · "laboratory"
• 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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