Bayesian and Modern Statistical Data Analysis

Principles of data analysis and modern statistical modeling. Exploratory data analysis. Introduction to Bayesian inference, prior and posterior distributions, predictive distributions, hierarchical models, model checking and selection, missing data, introduction to stochastic simulation by Markov Chain Monte Carlo using a higher level statistical language such as R or Matlab. Applications drawn from various disciplines. Not open to students with credit for STA 360 (STA 122). Prerequisite: STA 611 (STA 213) or Instructor consent. Instructor: Clyde or Reiter