Ahmad Bashir (University of Texas at Dallas, USA), Latifur Khan (University of Texas at Dallas, USA) and Mamoun Awad (University of Texas at Dallas, USA)
Copyright: © 2005
A Bayesian network is a graphical model that finds probabilistic relationships among variables of a system. The basic components of a Bayesian network include a set of nodes, each representing a unique variable in the system, their inter-relations, as indicated graphically by edges, and associated probability values. By using these probabilities, termed conditional probabilities, and their interrelations, we can reason and calculate unknown probabilities. Furthermore, Bayesian networks have distinct advantages compared to other methods, such as neural networks, decision trees, and rule bases, which we shall discuss in this paper.