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.
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 will be posted as they are announced.
Notes from previous years are included here for reference. Updated notes will be posted before 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://mason.gmu.edu/~klaskey/GraphicalModels/.
Organizations, Data Sets and Other Resources
Association for Uncertainty in Artificial IntelligenceMiscellaneous:
Bayesian Network Repository
UCI Machine Learning Repository
Proceedings Archive - Conference on Uncertainty in Artificial IntelligenceBayesians Worldwide - Links to web pages of BayesiansSoftware:
Probability Theory: The Logic of Science (classic book by the late E.T. Jaynes)
The Bayesian Songbook (includes Frequentist Frenzy by world renowned songwriter Kathryn Blackmond Laskey)Software for Belief Networks - Links to software tools for Bayesian networks
UnBBayes - Open source software for modeling, learning and reasoning with probabilistic graphical models
Netica - Software for Bayesian networks (commercial)
Hugin - Software for Bayesian networks (commercial)
GeNIe & SMILE - Inference and Decision Making Software
SamIam (Sensitivity Analysis, Modeling, Inference and More) - Software for Bayesian networks (freeware)
bnlearn - R package for structure learning, parameter learning, and inference
Kevin Murphy's Bayes Net Toolbox - General purpose MATLAB toolbox for Bayesian networks (freeware)
Probabilistic Programming resource page
Primula - Software for Relational Bayesian Networks (freeware)