Exercise: Cumulative Penality Heuristic

Now we explore one ad-hoc methodology to simulate databases for training Bayesian networks. This approach inspires us to think creatively and strategically about generating databases to train our BN effectively.

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 ...