Introduces the Bayesian approach to statistical inference for data analysis in a variety of applications. Topics include: comparison of Bayesian and frequentist methods, Bayesian model specification, choice of priors, computational methods such as rejection sampling, and stochastic simulation (Markov chain Monte Carlo), empirical Bayes method, hands-on Bayesian data analysis using appropriate software. Prerequisite: Math 309, Math 493 and either Math 3200 or 494; and some acquaintance with fundamentals of computer programming (such as CSE 131 or CSE 200), or permission of instructor.
Course Attributes: FA NSMAR NSMAS NSM
Section 01Bayesian Statistics
INSTRUCTOR: KuffnerView Course Listing