Course Schedule and Catalog

Long Title: GRAPHICAL MODELS AND NETWORKS

Department: Statistics

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Prerequisite(s): STAT 431

Description: Graphical models – aka Bayes networks, Markov networks, Gaussian networks, etc. – have been widely used to represent complex phenomena with dependence. The course aims to stimulate interest in graphical models and covers directed and undirected graphical models, exponential-family representations of graphical models, statistical inference, finite-sample and large-sample properties, and applications.