Subject
STAT - Statistics
Description
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.
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.
GFC Hours
(3-0)
Domestic Fee Rate Group
A
International Fee Rate Group
B
Signature Learning
Research & Creative Scholarship
Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.
Component
LEC
Units
3
Repeat for Credit
No