STAT 425 - Computational Statistics

When Offered: F, S

3 Credit(s)
3 Lecture Hour(s)
0 Lab Hour(s)

This course introduces students to various computationally intensive statistical techniques. Topics will include numerical optimization for statistical inference (gradient-based optimization, the Expectation-Minimization (EM) algorithm, and Fisher scoring), random number generation, resampling methods such as the bootstrap, permutation and randomization tests, cross-validation, Markov Chain Monte Carlo techniques (Gibbs sampling and Metropolis-Hastings algorithm), and nonparametric curve fitting. Students will learn to apply these techniques to solve data science problems using the statistical software R.

Prerequisite(s): STAT 328 or STAT 324.