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A platform for research: civil engineering, architecture and urbanism
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.
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.
Construction of Simulation for Probabilistic Inference in Uncertain and Dynamic Networks Based on Bayesian Networks
Ding, Jianguo (author) / Jiang, Ningkang (author) / Li, Xiaoyong (author) / Kramer, Bernd (author) / Davoli, Franco (author) / Bai, Yingcai (author)
2006-06-01
4917463 byte
Conference paper
Electronic Resource
English
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