Description: Computationally intensive statistical methods. Topics include statistical learning (cross-validation, GAMs, classification and regression trees), nonparametric methods (bootstrapping and permutation tests) as well as general-purpose algorithms (MCMC). Emphasis placed on both underlying statistical concepts and implementing resulting algorithms in a high-level mathematical programing language on non-tidy data. Co-convened with STA 478. Letter grade only. Course fee required.
Sections offered: Fall 2020
Prerequisite: ((STA 471 or STA 571 with a grade of C or better) and STA 445), or admission to Statistics MS