Prerequisites: Linear algebra, 6.436 or equivalent (upper division probability/statistics)
Instructor: Professor Tamara Broderick
Schedule: TR2:30-4, room 34-301
This subject qualifies as a Communications concentration subject.
A graduate-level introduction to theoretical statistics, covering both frequentist and Bayesian aspects of modeling, inference, and decision-making. Topics include statistical decision theory; point estimation; exponential families; Bayesian methods; empirical and hierarchical Bayes; hypothesis testing; confidence intervals; asymptotics; M-estimation; James-Stein theory; high-dimensional regression and covariance estimation. Pre-requisites include linear algebra, 6.436 or equivalent upper-division probability/statistics; real analysis is a plus.