Computational Methods in Statistics

The power of modern computers has unleashed new ways of thinking about statistics and implementing statistical solutions. This course introduces the student to computational techniques with uses ranging from exploratory data analysis to statistical inference. These techniques are now widely used and are fast becoming indispensable in the modern statistical toolkit. The course will provide the student with a sound understanding of the computational methods and hands-on experience in implementing and using them. Topics include generation of random variables, Monte Carlo integration and importance sampling, bootstrap methods, Markov chain Monte Carlo, kernel density estimation and regression, classification and regression trees.