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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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An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning
Reliability Engineering and System Safety, 2021Qing Yu, Hao Rong, Carlos Guedes Soares
exaly
Time Series: Dynamic Bayesian Networks
2021Marco Scutari, Jean-Baptiste Denis
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Dynamic Bayesian networks with application in environmental modeling and management: A review
Environmental Modelling and Software, 2023Huaiwei Sun
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Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)
Reliability Engineering and System Safety, 2006Philippe Weber, Lionel Jouffe
exaly