Naïve Bayes is a simple probabilistic classifier based on Bayes' theorem, while Bayesian networks are a graphical representation of probabilistic relationships among variables. Therefore, Naïve Bayes is a specific algorithm that can be used within Bayesian networks.

Bayesian networks consist of nodes representing variables and directed edges representing dependencies between variables. The process of developing a Bayesian networks model typically involves the following steps:

1. Define the problem: Clearly specify the problem statement and the variables involved.

2. Identify variables: Determine the key variables related to the problem and their relationships. This may involve domain knowledge or data analysis.

3. Structure learning: Decide the structure of the Bayesian network by understanding the dependency relationships between variables. This can be done using expert knowledge, literature review, or data-driven algorithms.

4. Parameter learning: Assign probabilities to the variables within the Bayesian network. This can be done by either using historical data or expert judgments.

5. Validation: Validate the model against a testing dataset to ensure its accuracy and adjust any necessary parameters or structure.

6. Inference: Utilize the Bayesian network to make predictions or infer values for specific variables based on observed evidence.

7. Maintenance and updating: Update the model with new data or changes in the domain knowledge.

It's important to note that developing a Bayesian network model requires a combination of statistical knowledge, domain expertise, and understanding of the problem at hand.

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