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Nonhomogeneous Dynamic Bayesian Networks in Systems Biology
2011Dynamic Bayesian networks (DBNs) have received increasing attention from the computational biology community as models of gene regulatory networks. However, conventional DBNs are based on the homogeneous Markov assumption and cannot deal with inhomogeneity and nonstationarity in temporal processes.
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Dynamic and Temporal Bayesian Networks
2015Dynamic Bayesian network models extend BNs to represent the temporal evolution of a certain process. There are two basic types of Bayesian network models for dynamic processes: state based and event based. Dynamic Bayesian networks are state-based models that represent the state of each variable at discrete time intervals.
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Traffic congestion propagation inference using dynamic Bayesian graph convolution network
Transportation Research Part C: Emerging Technologies, 2022Ruimin Ke, Xiaolei, Zhou Huang
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System operational reliability evaluation based on dynamic Bayesian network and XGBoost
Reliability Engineering and System Safety, 2022Hongdong Wang, Qing Li
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Time Series: Dynamic Bayesian Networks
2021Marco Scutari, Jean-Baptiste Denis
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Dynamic safety assessment of natural gas stations using Bayesian network
Journal of Hazardous Materials, 2017Esmaeil Zarei, Ali Azadeh, Nima Khakzad
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Dynamic availability assessment of safety critical systems using a dynamic Bayesian network
Reliability Engineering and System Safety, 2018Faisal Khan, Syed Ahmad Imtiaz
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