MATH 340 · also MATH 740, STA 231
Advanced Introduction to Probability
Alexander Dunlap
Description from DukeHub
Advanced introduction to basic, non-measure theoretic probability covering topics in more depth and with more rigor than MATH 230. Topics include random variables with discrete and continuous distributions. Independence, joint distributions, conditional distributions, generating functions, Bayes' formula, and Markov chains. Rigorous arguments are presented for the law of large numbers, central limit theorem, and Poisson limit theorems. Prerequisite: Mathematics 202, 212, 219, or 222. Not open to those who have taken Mathematics 230 or Statistics 230.
Skills
- data and statisticsdescription: "probability" and 2 more
AI-resilient: 1 of 5
- No: Physical, lab or field work
- No: Live work with people
- Yes: Judgment someone is accountable for · "law"
- 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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