Case Studies in Bayesian Statistics

Description from DukeHub

Advanced Bayesian statistical modelling from an applied perspective; problems and data from a range of application areas; focus on statistical thought and practice with in-depth examination of applications; statistical topics drawn from multilevel modelling, randomization and experimental design, causal inference, meta analysis, item response models, models for categorical data, time series, model assessment and criticism, scientific communication. Instructor consent required. Recommended prerequisites 602, 702L or equivalent and STA721L.

Skills

• data and statisticsdescription: "bayesian" and 5 more

AI-resilient: 0 of 5 0/5

• 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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