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Mathematics Courses


Mathematics And Statistics 494 - Spring 2020

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: Math 3200 and 493, or permission of the instructor.
Course Attributes: FA NSMAR NSMAS NSM

Section 01

Mathematical Statistics
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