BAYESIAN AND MODERN STATISTICS

STA 601

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 Statistical Science 360. Instructor: Clyde, Dunson, Reiter, or Volfovsky

Day / Time: 

F 10:05 AM-11:20 AM

Location: 

Old Chem 101

Instructor: 

Volfovsky, Alexander

Section: 

01L