From Certainty Factor Model to Bayesian Network
Rong Wang · 2004
In this paper, the relations and differences between certainty factor model and Bayesian Network are researched. Firstly, the limitation of the theoretical basis of certainty factor model is discussed; it is proved that the certainty factor model implies a conditional independence hypothesis same as the simple Bayesian model implies. Then, some functions of Bayesian Network, which correspond to certainty factor model, are explored. The concepts, analysis approach, and computing formula of condition's influence degree and effect direction to the inference conclusion in Bayesian network are presented, and it is also proved that the equivalence of the probabilistic inference between the Noisy-OR model and certainty factor model. Finally, the superiority of Bayesian network to certainty factor model is discussed in the aspects of representation, inference, and acquire of the knowledge. Our conclusion is that the Bayesian network not only has the main function of the certainty factor model, but also can break through some limitation of this model, so the Bayesian network can be a main trend probabilistic model in intelligent information process instead of the certainty factor model eventually.