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: CSE 131, Math 309, (Math 493 or Math 3211), (Math 3200 or Math 494 or Math 4211).
Course Attributes: FA NSM; AR NSM; AS NSM
