Lecturers:
Uffe Kj�rulff, Aalborg University and Kristian Olesen, Aalborg UniversityThe Aim of this Short Course:Bayesian networks are graphical models of non-deterministic impact between variables and events. These relations are described by conditional probability tables. If decisions are added to the models they are known as influence diagrams. The formalisms rely on a coherent probability theoretic foundation, thus they are particular well suited for systems where uncertainty plays an important role. The graphical representation makes models easy to understand and enable immediate investigation of the effects of information and intervention. These effects are displayed as updated posterior distributions for unknown variables given the states of some other variables. As effective algorithms exist for automatic updating of models, users need not worry much about details. Methods for automatic adaptation of the conditional probability tables are available, as are semi-automatic methods for identifying the structure of models. The technology has matured to a stage where it has been applied to various practical problems, such as forecasting, diagnosing and planning. In this three day course the basics of Bayesian networks and influence diagrams will be presented, including examples of applications in agriculture, medicine, genetics and fault repair in computer equipment. The course includes hands-on experience with HUGIN, an automated tool for construction and execution of models. During these sessions the participants will be given the opportunity to work on their own problems.