Transform Descriptive Graphs into Bayesian Networks

In this lesson, we will discuss the process of converting a descriptive graph into a Bayesian network, a probabilistic graphical model that represents the relationships between random variables and their probabilities.

First, let's recall some important concepts linked to the concept of BN:

• Directed graph: The edges in a Bayesian network are directed, meaning they have a specific direction, originating from one node (the parent node) and pointing towards another node (the child node). This directionality indicates the flow of influence or causality between the nodes. A directed edge from node A to node B implies that the probability distribution of node B depends on the value of node A. The directed nature of the edges allows us to represent conditional dependencies and model the causal relationships between variables.

• Acyclic graph: A Bayesian network is also acyclic, which means that it does not contain any cycles or closed loops. This constraint ensures that the relationships between the nodes are well-defined and prevents circular ...