BIOST 2131 - FOUNDATIONS OF STATISTICAL THEORY
Minimum Credits: 4
Maximum Credits: 4
The course covers the basic theory of probability and statistical inference with a focus on the appropriate use of standard methods and on the construction of new statistical inference tools. Topics covered in the first half include joint, marginal, and conditional probabilities; random variables and functions thereof; distribution characteristics of random variables; basic asymptotic theory and univariate theorems including Chebyshev’s inequality, the law of large numbers, and the central limit theorem. Topics covered in the second half include principles and methods of constructing estimators (e.g., MLE, MME, CRLB), confidence intervals, and hypothesis testing (including Neyman-Person and generalized likelihood ratio tests); data reduction principles and techniques, and their relationship to optimal statistical inference (including sufficiency and Rao-Blackwell principle); basic likelihood-based, exact, conditional, and asymptotic statistical inference. The course is taught through lectures and recitation sessions.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Course Requirements: PLAN: BIOST-MS or BIOST-PHD
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