DSC 335 Bayesian Data Analysis
The Bayesian statistical paradigm is an alternative to the frequentist paradigm prevalent in most introductory statistics courses. This course examines Bayes’ Theorem and the philosophy and history of Bayesian methods. It introduces Bayesian alternatives to frequentist inferential techniques. Topics include prior and posterior distributions, conjugate priors, hierarchical models, hypothesis testing, regression, and the Markov Chain Monte Carlo (MCMC) method. The course emphasizes statistical computing as well as real-world data analysis and communication. Pre-requisites: MAT 310, DSC 230, and MAT 165. Or, permission from the instructor.