Mastering Probabilistic Graphical Models Using Python

Dynamic Bayesian networks

In the examples we have seen so far, we have mainly focused on variable-based models. In these types of models, we mainly focus on representing the variables of the model. As in the case of our restaurant example, we can use the same network structure for multiple restaurants as they share the same variables. The only difference in all these networks would be the different states in the case of different restaurants. These types of models are known as variable-based models.

Let's take a more complex example. Let's say we want to model the state of a robot traveling over some trajectory. In this case, the state of the variables will change with time, and also, the states of some variables at some instance t might depend ...