CS539: Graphical Methods for Modeling, Inference, and Data Analysis

Course Description

Graphical methods have transformed probabilistic modeling and stand poised to revolutionize statistical data analysis. Graphical modeling (aka Bayesian networks, belief networks, chain graphs, undirected models, markov random fields, ...) provide a visual representation of highly-structured probabilistic models of compolkex domains, and have been used in medicine, science, engineering, and the military. Efficient methods exist for both model discovery and inference.

We will begin with a quick overview of the theory behind graphical probabilistic modeling. We will review basic representations, inference methods, and model discovery methods. Most of the course will be organized around a series of case studies. Anticipated case studies include: (1) medical diagnosis; (2) biological model discovery; (3) web visitor behavior modeling; (4) military situation assessment. Other topics will be explored as time permits and class interest dictates.

Prerequisites include basic understanding of probability and elementary statistics, together with experience and/or interest in system modeling.

Faculty: Bruce D'Ambrosio, 107 Dearborn, 737 5563, dambrosi@cs.orst.edu
Registration Information: 4 Units. MWF 4-5pm GBAD 103

Class Materials

Class materials will consist of a series of readings to be handed out in class. We will also be utilizing a number of research and commercial software systems for graphical modeling.

Current reading list is HERE


Bruce D'Ambrosio, dambrosi@cs.orst.edu