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STAT 456 Bayesian Analysis Units: 3.00
An introduction to Bayesian analysis and decision theory; elements of decision theory; Bayesian point estimation, set estimation, and hypothesis testing; special priors; computations for Bayesian analysis. Given Jointly with STAT 856.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Requirements: Prerequisite STAT 463 or permission of the Department.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Demonstrate proficiency in finding the Fisher information contained in the data about unknown parameters.
- Find Bayesian estimators for different functions of unknown parameters, under various loss functions.
- Find the best unbiased estimators in the Hardy-Weinberg genetic equilibrium model.
- Identify least informative prior distributions of unknown parameters and the resulting minimax admissible procedures.