Description: Computationally intensive statistical methods. Topics include nonparametric methods such as bootstrapping and permutation tests, and general-purpose algorithms such as Markov chain Monte Carlo and gradient descent. Also covered are practical issues about data storage, verification, and cleaning. Emphasis placed on both underlying statistical concepts and implementing resulting algorithms in a high-level mathematical programing language. Letter grade only. Course fee required.
Sections offered: Fall 2019
Prerequisite: (STA 371 or 471 or 571) or (CS 122 or instructor consent) or admission to Statistics MS