Discussion (Chapter 5): What Is The Relationship Between Naï ✓ Solved

Discussion (Chapter 5): What is the relationship between Naïve Bayes and Bayesian networks?

Create a discussion thread (with your name) and answer the following question:

Discussion (Chapter 5): What is the relationship between Naïve Bayes and Bayesian networks? What is the process of developing a Bayesian networks model?

Note: The first post should be made by Wednesday 11:59 p.m., EST.

I am looking for active engagement in the discussion. Please engage early and often. Your response should be words. Respond to two postings provided by your classmates. There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations.

Sample Paper For Above instruction

Understanding the Relationship Between Naïve Bayes and Bayesian Networks and the Process of Developing Bayesian Models

Bayesian networks and Naïve Bayes classifiers are fundamental tools in probabilistic modeling and machine learning. While they are interconnected, they serve distinct purposes within the realm of probabilistic reasoning. This discussion explores the relationship between these two models and outlines the process involved in developing a Bayesian network model.

The Relationship Between Naïve Bayes and Bayesian Networks

Naïve Bayes classifiers are specialized types of Bayesian networks. Both models use probabilistic graphical structures to represent relationships among variables. The primary distinction is that Naïve Bayes assumes conditional independence among predictor variables given the class label, simplifying the network structure to a single node for the class and multiple feature nodes connected directly to it. This assumption significantly reduces computational complexity and facilitates faster classification (Murphy, 2012). Conversely, Bayesian networks are more flexible and can model complex dependencies among variables through directed acyclic graphs (DAGs). This flexibility enables Bayesian networks to represent a broader range of relationships and dependencies that are not necessarily assumed to be independent or conditionally independent.

The Process of Developing a Bayesian Networks Model

Developing a Bayesian network involves several steps. First, domain experts and data analysts collaboratively identify relevant variables and potential dependencies based on domain knowledge. Next, the network structure is designed, specifying nodes (variables) and directed edges that reflect hypothesized causal or influential relationships (Heckerman et al., 1995). The structural design is followed by parameter learning, where conditional probability tables (CPTs) are estimated using training data or expert judgment. Techniques such as maximum likelihood estimation or Bayesian estimation are typically employed for this purpose (Koller & Friedman, 2009). After the parameters are acquired, the model is validated through various methods, including cross-validation or inference checks, to ensure that it accurately captures the underlying data patterns. Once validated, the Bayesian network can be used for probabilistic inference, decision-making support, and prediction tasks.

Conclusion

In summary, Naïve Bayes classifiers are specific instances of Bayesian networks characterized by simplified independence assumptions, making them computationally efficient but limited in modeling complex relationships. Bayesian networks themselves are versatile frameworks capable of representing intricate probabilistic dependencies across variables. The development of a Bayesian network involves structuring the network based on domain knowledge, learning parameters from data, and validating the model's performance. These models are invaluable in various domains such as medicine, finance, and artificial intelligence, where understanding and reasoning under uncertainty are essential.

References

• Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197-243.
• Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. MIT Press.
• Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.