Preprocessing Data for Creating Bayesian Networks

In this lesson, we will explore the conversion of networks into Bayesian networks, where nodes represent random variables and edges represent conditional dependencies. We'll build a suitable database for training a Bayesian network and understanding the structure and relationships between nodes.

Let’s imagine this scenario: We are city planners for a small town with ten distinct locations (nodes) connected by roads (edges). The locations are represented by letters A to J, and the roads have different distances (weights) between them. The town map and distances between locations are as follows:

When converting a network into a Bayesian network, each node represents a random variable, and each edge represents a ...