Statistical Computing II

3 credits · Undergraduate

(3 hrs Lecture) This course introduces modern techniques of statistical computing for practical analysis of data using the statistical software R. Topics include random number generation, an introduction to resampling methods, classical and Bayesian inference and Monte Carlo methods, methods of finding roots, numerical integration methods, expectation-maximization, and Markov chain Monte Carlo. All statistical principles are illustrated using simulated and real-life data sets. Requisite Courses: STAT-2903 or STAT-3103 [prerequisite(s)]; STAT-3612 or MATH-3612 (must be taken previously or at the same time as this course).