- Lecture/Discussion: 3
- Lab: 1.5 (biweekly)
This course covers the implementation of a variety of computationally intensive statistical techniques. These include generation of random variables, vectors and processes; randomization methods; bootstrap methods; Markov chain Monte Carlo (MCMC) methods; numerical optimization in statistical inference [expectation-maximization (EM) algorithm, Fisher scoring, etc.].