— Computational Statistics

Prerequisites & course information

This version has ended; see updated course, below. Course Description: Numerical analysis; random number generation; computer experiments and resampling techniques (bootstrap, cross validation); numerical optimization; matrix decompositions and linear algebra computations; algorithms (markov chain monte carlo, expectation-maximization); algorithm design and efficiency; parallel and distributed computing. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Grade Mode: Letter. STA 243 — Computational Statistics (4 units) Course Description: Numerical analysis; random number generation; computer experiments and resampling techniques (bootstrap, cross validation); numerical optimization; matrix decompositions and linear algebra computations; algorithms (markov chain monte carlo, expectation-maximization); algorithm design and efficiency; parallel and distributed computing. Prerequisite(s): ( STA 130A , STA 130B ); ( MAT 067 or MAT 167 ); or equivalent of STA 130A and 130B , or equivalent of MAT 167 or MAT 067 . Learning Activities: Lecture/Lab 4 hour(s). Grade Mode: Letter. This course version is effective from, and including: Spring Quarter 2027. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Grade Mode: Letter. STA 243 — Computational Statistics (4 units) Course Description: Numerical analysis; random number generation; computer experiments and resampling techniques (bootstrap, cross validation); numerical optimization; matrix decompositions and linear algebra computations; algorithms (markov chain monte carlo, expectation-maximization); algorithm design and efficiency; parallel and distributed computing. Prerequisite(s): ( STA 130A , STA 130B ); ( MAT 067 or MAT 167 ); or equivalent of STA 130A and 130B , or equivalent of MAT 167 or MAT 067 . Learning Activities: Lecture/Lab 4 hour(s). Grade Mode: Letter. This course version is effective from, and including: Spring Quarter 2027.