Restricted to students in the PhD Statistics program. An introduction to core applied statistical modeling ideas from a probabilistic, Bayesian perspective. Topics include exploratory data analysis, programming in R, Bayesian probability models, an introduction to the Gibbs sampler, applied regression analysis, and hierarchical models. Three lecture hours a week for one semester. Statistics and Data Sciences 383C and Statistics and Scientific Computation 383C may not both be counted. Prerequisite: Graduate standing.