dynamic_bns
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| Bayesian Networks usually represent a static state of the studied system, and **feedback loops** cannot be incorporated. To take into account changes over time, including feedbacks, we can use dynamic BNs, using the so-called “time-slicing” approach. In practice, this means that each time step is represented by a run of the network, where the **outputs of one step are used as inputs to the next** time step. | Bayesian Networks usually represent a static state of the studied system, and **feedback loops** cannot be incorporated. To take into account changes over time, including feedbacks, we can use dynamic BNs, using the so-called “time-slicing” approach. In practice, this means that each time step is represented by a run of the network, where the **outputs of one step are used as inputs to the next** time step. | ||
dynamic_bns.1545067007.txt.gz · Last modified: 2023/04/21 15:30 (external edit)