Probability and Statistical Models

Description from DukeHub

Theory, modeling, and computational topics in probability and statistics: distribution theory and modeling, simulation and applied probability models in statistics. Monte Carlo method and integration; Markov Chain Monte Carlo methods; applied stochastic processes including Markov process theory, linear systems theory, and AR models. Latent variable probability models, i.e., mixture models, hidden Markov models, and missing data problems. Discrete and continuous multivariate distributions; graphical models; tools of linear algebra and probability calculus. Statistical computing using Matlab/R. Prerequisite: Statistical Science 702L and 721L. Recommended prerequisite: Statistical Science 732.

Skills

• data and statisticsdescription: "modeling" and 10 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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