OR/STAT 719 CSI 775:
Computational Models For Probabilistic Reasoning

Spring 2008

This course is dedicated to the memory of journalist Danny Pearl, murdered in Pakistan in February 2002, and to the pioneering research of his father Judea Pearl. Judea Pearl’s research has the potential to create unprecedented advances in our ability to anticipate and prevent future terrorist incidents. May Judea’s research be applied to realize Danny’s vision of a world where people of all cultures live together in peace, harmony, and mutual respect.

Course Description

Intelligent computer agents must perform goal-directed action in complex, uncertain, and dynamic environments. Agents tasked with problems of any complexity must have methods for handling uncertainty. This course examines theory and methods for building computationally efficient software agents that reason, act and learn in environments characterized by noisy and uncertain information. The course covers methods based on graphical probability and decision models. The course studies approaches to representing knowledge about uncertain phenomena, drawing inferences about uncertain phenomena, and planning and acting under uncertainty. Theory, practical tools, and hands-on experience are provided. Students learn graph theoretic concepts for representing conditional independencies among a set of uncertain hypotheses. Students study exact and approximate methods for updating probabilities to incorporate new information. Practical model building experience is provided. Probabilistic and decision theoretic approaches to major areas of artificial intelligence such as knowledge representation, machine learning, data mining, case-based reasoning, planning, and temporal reasoning are discussed. Students apply what they learn to a semester project of their own choosing.

Prerequisites:

The listed prerequisites are STAT 692 or SYST/STAT 664 or permission of instructor. Students are expected to have a strong grounding in calculus-based probability theory, to have graduate-level mathematical sophistication, to be competent at building mathematical models and deriving conclusions from the models, to be comfortable performing statistical analysis of a data set and deriving conclusions from the analysis, and to be comfortable with software tools for mathematical modeling such as Matlab, S+, or Mathematica. I will be glad to work with any student to evaluate whether the student's background is appropriate for this course.

Requirements:

Grades will be based on the following:

Assignments 30%
Take-Home Midterm 20%
Take-Home Final 20%
Project 30%

Text:

There is no required text for this course. I teach from notes. I strongly recommend purchasing one of the following references:

Learning Bayesian Networks by Richard Neapolitan, Prentice Hall, 2003
Bayesian Artificial Intelligence by Kevin Korb and Ann Nicholson, Chapman and Hall, 2003
Bayesian Networks and Decision Graphs (2nd edition) by Finn Jensen, Springer, 2007

Students are permitted to work together on assignments, but your write-up must be your own. Assignments are intended to provide practical, hands-on experience with the ideas presented in the course. Assignments will be posted on this site. Students will have at least 2 weeks to complete each assignment. Late assignments receive half credit. The take-home exam must be done individually without collaboration.

Class Email List

I communciate with students through a class listserv. All currently registered students have been subscribed with the email address you provided to the university. If you wish to subscribe with a different address, please visit the listserv site and subscribe yourself, or send me an email with the address from which you wish to subscribe.

Project

The best way to learn something is to apply it. Your project is an excellent opportunity to apply what you are learning to a problem of your own choice and obtain free help and support from the instructor and your fellow seminar participants.

Assignments

Assignments will be posted as they are announced.

Assignment 1: Due February 7, 2008
Assignment 2: Due February 14, 2008
Assignment 3: Due February 21, 2008
Assignment 4: Due February 28, 2008
Midterm exam: Due March 20, 2008
Assignment 5: Due April 3, 2008
Assignment 6: Due April 12, 2008

Study Aids

From time to time, I post examples and solutions to exercises.

Lecture Notes

Notes from previous years are included here for reference. Updated notes will be posted by 9:00 AM the morning of class.

If you find these lecture notes helpful and use them in your work, please provide the appropriate citation: Laskey, K.B., Lecture Notes on Computational Models of Probabilistic Inference, Fairfax, VA: George Mason University, http://ite.gmu.edu/~klaskey/CompProb/.

Unit 1: Course Overview and Introduction ** Spring 2008 version**
Unit 2: Graph Theory, Conditional Independence and Bayesian Networks ** Spring 2008 version**
Unit 3: Representing Knowledge in an Uncertain World ** Spring 2008 version**
Unit 4: Belief Propagation in Bayesian Networks: Propagation in Junction Trees ** Spring 2008 version**
Unit 5: Learning Bayesian Networks from Data ** Spring 2008 version**
Unit 6: Knowledge Engineering ** Spring 2008 version**
Unit 7: Belief Propagation in Bayesian Networks: Other Inference Algorithms ** Spring 2008 version**
Unit 8: Planning and Decision Making ** Spring 2008 version**
Unit 9: Inference in Dynamic Bayesian Networks ** Spring 2008 version**

Useful Links

If you discover broken links, please let me know and I will fix them. Also, I am glad for suggestions of additional useful links.

Organizations, Data Sets and Other Resources
Association for Uncertainty in Artificial Intelligence
Bayesian Network Repository
UCI Machine Learning Repository
Proceedings of the Conference on Uncertainty in Artificial Intelligence
Miscellaneous:
Bayesians Worldwide - Links to web pages of Bayesians
Russ Greiner's BN Page - Links to useful information on Bayesian networks
Probability Theory: The Logic of Science (excellent book by the late E.T. Jaynes)
Bayesian Model Averaging (tutorial paper for Statistical Science by Hoeting, Madigan, Raftery, and Volinsky)
The Bayesian Songbook (includes Frequentist Frenzy by world renowned songwriter Kathryn Blackmond Laskey)
Software:
Software for Belief Networks - Links to software tools for Bayesian networks
Kevin Murphy's Bayes Net Toolbox - General purpose MATLAB toolbox for Bayesian networks (freeware)
Netica - Software for Bayesian networks (commercial)
Hugin - Software for Bayesian networks (commercial)
SamIam (Sensitivity Analysis, Modeling, Inference and More) - Software for Bayesian networks (freeware)
Bayes Net Power Constructor - Learns BNs from data
IBAL - A General Purpose Language for Probabilistic Reasoning & Decision Theoretic Agents
Primula - Software for Relational Bayesian Networks (freeware)
GeNIe & SMILE - Software for Bayesian networks (freeware)
Proximity - Software for relational knowledge discovery (freeware)

OR/STAT 719 CSI 775: Computational Models For Probabilistic Reasoning