Bayesian Statistics

Catalog description

This course comprehensively covers Bayesian statistics from its introduction to advanced computing and model building. Topics covered include distribution theory, conjugate models, hierarchical models, and statistical simulation and computing with Markov Chain Monte Carlo (MCMC). Students will also learn the foundation of statistical model building and diagnostics. With a balance of theoretical rigor and practical applications, the class will culminate in a course project to address real world problems. Satisfies Data Science minor requirement. Prerequisites & Notes A semester of college-level introductory statistics course in Sociology, Economics, Political Science, Psychology, or Mathematics, such as SOC 201, ECO 204, POL 182, or MAT 341. Proficiency in calculus (e.g. Math 113 or MAT140). Strong foundational knowledge in R or CSC 121.