Statistical Computing

Catalog description

The course will focus on fundamental statistical computation techniques and algorithms widely used in modern statistical methods. Key topics include simulation and sampling from probability distributions, Monte Carlo methods including Markov-chain Monte Carlo, resampling methods including bootstrapping and jackknifing, missing data imputation methods, EM algorithm, nonparametric curve fitting and smoothing, and density estimation. Students will learn to implement these computational methods using R statistical software. Emphasis will be given to reproducible research and reproducible dynamic report generation using R markdown. Offered as STAT 327 and STAT 427 . Prereq: MATH 380 or STAT 301 or STAT 312 or STAT 313 or STAT 325 or STAT 345 or STAT 425 or STAT 445 .