Prerequisite: STAT 544 or 554 or equivalent, or permission of instructor.
Course Description: Introduces
decision theory and relationship to Bayesian statistical
inference. Teaches commonalities, differences between
Bayesian and frequentist approaches to statistical inference,
how to approach a statistics problem from the Bayesian
perspective and how to combine data with informed expert
judgment in a sound way to derive useful and policy-relevant
conclusions. Teaches necessary theory to develop firm
understanding of when and how to apply Bayesian and
frequentist methods, and practical procedures for inference,
hypothesis testing, and developing statistical models for
phenomena. Teaches fundamentals of Bayesian theory of
inference, including probability as a representation for
degrees of belief, likelihood principle, use of Bayes Rule to
revise beliefs based on evidence, conjugate prior distributions
for common statistical models, and methods for approximating
the posterior distribution. Introduces graphical models
for constructing complex probability and decision models
from modular components. s