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. Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 1), Statistics and Data Sciences 383C, Statistics and Scientific Computation 383C. Prerequisite: Graduate standing.