Bayesian Nonparametric Models and Methods

Description from DukeHub

Modern nonparametric approaches to statistical analysis. Infinite dimensional Bayesian models: data analysis, inference and prediction. Models of curves, surfaces, probability distributions, partitions and latent feature spaces; nonparametric density estimation, regression and classification; hierarchical, multivariate and functional data analysis models; theory of estimation in function spaces. Methodology of probabilistic process models: Dirichlet, Gaussian, basis/kernel expansion, splines, wavelets, support vector machines and other local regression models. Interfaces of Bayesian:non-Bayesian methods and additional methodological topics. Prerequisite: Statistical Science 732 and 831.

Skills

• data and statisticsdescription: "statistical" and 9 more
• research methodsdescription: "methodology" and 1 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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