Summary, Main Concepts, and Takeaways

Bayesian networks building criteria

We have seen in this chapter several concepts related to the structural-based parameters of Bayesian network models. We want to emphasize these criteria as they are very important to keep in mind when building more complex models.

For this, we will synthesize all our work and evaluate these criteria in the case of the rain model.

Ensure semantic consistency

The Bayesian network should be easily interpretable by experts, providing understandable and useful information that aligns with their knowledge. Maintaining semantic consistency enhances interpretability, comprehension, and consistency over time.

Adjust network completeness

The network should have an appropriate number of nodes and states to represent the concepts accurately. This criterion depends on the data's availability, completeness, and accessibility.

The Bayesian network generated on the rain model meets the Adjust Network Completeness criteria by having an appropriate number of nodes (cloudy, sprinkler, rain, wet_grass) and states (True, False) that accurately represent the concepts. The network structure captures the dependencies between the variables, ensuring an accurate representation of the relationships in the data.

Guarantee result relevance

The target node and its states should be useful for decision-making while maintaining sufficient accuracy and precision in their values.