Neural belief network
In this paper, a new neural belief network, which has considered backward inferences and the influence of the belief sources on belief propagations, is developed. In this new neural network, a link record set is built for every conclusion node for handling the multiple conditions of inference rules, and a route record set is built for every active node and every active link for handling the dependency of belief propagations on the belief sources. In addition, a temporary node is added for every evidence node. The assignment of the temporary nodes releases the evidence nodes from the role as belief sources and allows belief propagations in them. As a result, the new neural belief network can handle both definite evidences and indefinite evidences, and the evidences may come from observations or the prior knowledge of experts. The inference processes of the new neural belief network are based on available evidences and ifthen rules. Therefore, it can solve the problems of Bayesian networks caused by the prior knowledge reliance and may be an alternative technique to the popular Bayesian networks.
belief inference neural network bayesian networks connectionism