Abstract
Dynamic 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. Event-based models represent the changes in the state of each state variable; each temporal variable will then correspond to the time in which a state change occurs. In this chapter, we will review dynamic Bayesian networks and event networks, including representation, inference, and learning. The chapter includes two application examples: dynamic Bayesian networks for gesture recognition and temporal nodes Bayesian networks for HIV mutational pathways prediction.
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Sucar, L.E. (2015). Dynamic and Temporal Bayesian Networks. In: Probabilistic Graphical Models. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6699-3_9
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DOI: https://doi.org/10.1007/978-1-4471-6699-3_9
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