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.
This course may not be repeated for credit.
Prerequisite(s)
- Admission to a graduate program in Mathematics and Statistics or consent of the Department.
SyllabusSections
This course will be offered next in
Winter 2022.