CSCE 582 {=STAT 582} Bayesian Networks and Decision Graphs

Prerequisites: CSCE 350 (Data Structures and Algorithms) and STAT 509 (Statistics for Engineers)

Bulletin Description: Bayesian Networks and Decision Graphs. {=STAT 582} (3) (Prereq: CSCE 350 and STAT 509) Normative approaches to uncertainty in Artificial Intelligence. Probabilistic and causal modeling with Bayesian networks and influence diagrams. Applications in decision analysis and support. Algorithms for probability update in graphical models.

Meeting Time and Place: TTH 930-1045, SWGN 2A24
Instructor: Marco Valtorta
Office: Swearingen 3A55, 777-4641
Office Hours: MWF 1100-1200. Please check by phone or email. Others by appointment.

The goals of this course are:

The course is foundational. It concentrates on modeling and use of decision analysis principles. Algorithms for belief updating (especially variable elimination and Jensen's version of the Lauritzen-Spiegelhalter algorithm, but also stochastic simulation) are discussed to some depth, but advanced topics on algorithmic issues are left out. It is my hope that a student who successfully completes this course will both be able to use decision analytic tools such as Hugin well and be well prepared for advanced graduate courses in, e.g., data mining.

Grading Policy

Syllabus and Required Text

Lecture Log

Lecture Notes

Sample midterm, with answers


Graduate Student Presentations


Some Useful Links Jiri Vomlel. "Probabilistic reasoning with uncertain evidence." Neural Network World, International Journal on Neural and Mass-Parallel Computing and Information Systems, Vol. 14, No. 5/2004, pp. 453-465 (local copy).

Sample Tests
Final Exam of fall 2010, with answers (doc)