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.