Statistical Modeling II
Use of structured, probabilistic models that incorporate multiple layers of uncertainty to describe real-world systems. Topics include multivariate normal distribution, mixture models, nonparametric Bayesian analysis, advanced hierarchical models and latent-variable models, generalized linear models, and advanced topics in linear and nonlinear regression. Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 2), Statistics and Data Sciences 383D, Statistics and Scientific Computation 383D. Three lecture hours per week for one semester.
Topics
- multivariate normal distribution
- mixture models
- nonparametric Bayesian analysis
- advanced hierarchical models and latent-variable models
- generalized linear models
- and advanced topics in linear and nonlinear regression
Topics explicitly listed in official catalogue description
Prerequisites & course information
Use of structured, probabilistic models that incorporate multiple layers of uncertainty to describe real-world systems. Topics include multivariate normal distribution, mixture models, nonparametric Bayesian analysis, advanced hierarchical models and latent-variable models, generalized linear models, and advanced topics in linear and nonlinear regression. Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 2), Statistics and Data Sciences 383D, Statistics and Scientific Computation 383D. Three lecture hours per week for one semester.
Sources
- Course catalogue ↗2026-2027