ABSTRACT
Bayesian networks are popular within the fields of artificial intelligence and data mining due to their ability to support probabilistic reasoning from data with uncertainty. They can represent the co-related relationships among random variables and the conditional probabilities of each variable from a given data set. With a network structure at hand, people can conduct probabilistic inference to predict the outcome of some variables based on the values of other observed ones. Hence, Bayesian networks are widely used in many areas, such as diagnostic and classification systems (Jensen, 1996; Andreassen, et al., 1987; Cheeseman, et al., 1988), direct marketing, and so on. They are also suitable for reasoning with incomplete information.
