Statistical Computing

Catalog description

This course concentrates on computational tools, which are useful for statistical research and for computationally intensive statistics. Through this course you will develop a knowledge base and skill set of a wide range of computational tools needed for statistical research. Topics include computer storage, architecture and arithmetic; random number generation; numerical optimization methods; spline smoothing and penalized likelihood; numerical integration; simulation design; Gibbs sampling; bootstrap methods; and the EM algorithm. Prerequisite: If course requirements not met, permission of instructor required.