Mathematical Statistics

Theory of estimation, minimum variance and unbiased estimators, maximum likelihood theory, Bayesian estimation, prior and posterior distributions, confidence intervals for general estimators, standard estimators and distributions such as the Student-t and F-distribution from a more advanced viewpoint, hypothesis testing, the Neymann-Pearson Lemma (about best possible tests), linear models, and other topics as time permits. Prerequisite: CSE 131 or 200, Math 3200 and 493, or permission of the instructor. Math 310 is recommended but not required.
Course Attributes: FA NSM; AR NSM; AS NSM

Mathematical Statistics

MATHEMATICS AND STATISTICS 494

Section 01