Monash University > School of Computer Science and Software Engineering > CSE458
In a wide variety of areas, including medical diagnosis, business investments, oil exploration, and weather prediction, people develop models to assist them in making rational decisions. This unit will provide an introduction to Bayesian models and how they can be used in the decision making process.
We begin with an introduction to decision analysis, motivating the use of subjective probabilities (beliefs) and performance measures (utilities) in decision making, and contrasting Bayesian methods with other approaches. We then introduce Bayesian networks, their inference techniques and approximation methods. Finally we describe how Bayesian networks can be extended to handle: dynamic domains, choices of actions, and utilities. Throughout the unit we illustrate the use of the Bayesian models in various applications, such as robotics and planning, medical decision making, intelligent tutoring, plan recognition, and game playing.
David Albrecht.
Room G10, Building 63,
Clayton Campus, Monash University.
Phone: (+61-3) 9905-5526
Fax: (+61-3) 9905-5146
Email: David.Albrecht@csse.monash.edu.au
Consultation hours:
- To be announced
Howard Raiffa (1970), Decision Analysis: Introductory Lectures on Choices under Uncertainity, Addison-Wesley.
Robert T. Clemen (1995), Making Hard Decisions: An Introduction to Decision Analysis, Duxbury Press.
Stuart Russell and Peter Novig (1995), Artificial Intelligence: A Modern Approach, Prentice Hall.
Richard E. Neapolitan (1990), Probabilistic Reasoning in Expert Systems: Theory and Algorithms, John Wiley & Sons, Inc.
Richard E. Neapolitan (2003), Learning Bayesian Networks , Prentice Hall. First 5 chapters are available here.
Finn V. Jensen (2001), Bayesian Networks and Decision Graphs, Springer-Verlag, Inc.
Kevin Murphy (2002), Dynamic Bayesian Networks: Representation, Inference and Learning, PhD Thesis, University of California, Berkeley.
Last modified 3/4/2003