Summary, Main Concepts, and Takeaways
Summarize the best practices for Bayesian network construction guaranteeing result relevance, limiting combinatorial explosion, and ensuring high-quality learning.
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. ...