====Iterative BNs====

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. 

For example, when modelling carbon storage over time, we start with an estimate of the forest biomass at the beginning of the first time step (//Biomass//). During one time step, growth and harvesting take place, and through inference we obtain the probability distribution of land use after the first time step (//Biomass1//, e.g. after 10 years). This //Biomass1// then becomes the input for //Biomass// in the second time step. 

In gBay, we create such a dynamic BN by specifying links across time steps. To do this, click the option **“Link”** on the output node that should feed into the input of the next time step (//Biomass1//). An orange arrow will appear, which should be connected to the corresponding input node (//Biomass//). 

{{:gbay_dynamic.png?900|}}