Computational Statistics

3 credits · Graduate Programs

Unconstrained optimization methods, simulation and random number generation, Bayesian inference and Monte Carlo methods, Markov chain Monte Carlo, non-parametric inference, classical inference and other topics. An emphasis will be placed on computational implementation of algorithms. Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.