Statistics 221 - Statistical Computing Methods
Instructor: Mark Irwin
A study of computing methods commonly used in statistics. Topics include generation of random numbers, Monte Carlo methods, optimization methods, numerical integration, resampling methods such as the Bootstrap and the Jackknife, and advanced Bayesian computational tools such as the Gibbs sampler, Metropolis Hastings, the method of auxiliary variables, marginal and conditional data augmentation, slice sampling, exact sampling, and reversible jump MCMC. Computer programming exercises apply the methods discussed in class.
Solutions for the first two assignments have been added to the Assignments page.