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. Prereq: CSE 131, (Math 493 or Math 3211). Math 310 is recommended.
Course Attributes: FA NSM; AR NSM; AS NSM

Section 01

Mathematical Statistics
INSTRUCTOR: Chakraborty
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