Construction of Simulation for Probabilistic Inference in Uncertain and Dynamic Networks Based on Bayesian Networks

Bayesian networks have been used widely in modelling complex network systems. Probabilistic inferences are normal requirements in control and management of networks, particularly in modelling diagnosis systems for large and complex networks in probabilistic and dynamic environments. This paper provides a random simulation mechanism to construct the simulation in Bayesian networks for probabilistic inference, so that the simulation in Bayesian networks is close to real life networks and further the intelligent decision in management of networks can be obtained.