Create A Discussion Thread With Your Name And Answer The Fol

Create A Discussion Thread With Your Name And Answer The Following Q

Create a discussion thread (with your name) and answer the following question: Discussion 1 (Chapter 5): What is the relationship between Naïve Bayes and Bayesian networks? What is the process of developing a Bayesian networks model? 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.

Paper For Above instruction

Relationship between Naïve Bayes and Bayesian networks and model development

Naïve Bayes classifiers and Bayesian networks are both probabilistic models rooted in Bayes’ theorem, but they differ significantly in their assumptions and complexity. Naïve Bayes is a simplified form of Bayesian network characterized by its assumption of feature independence given the class label, which allows for straightforward computation and efficient classification even with limited data (Russell & Norvig, 2020). Conversely, Bayesian networks embody a more flexible and detailed graphical model that represents variables and their probabilistic dependencies explicitly through directed acyclic graphs (Pearl, 1988). The relationship between the two lies in the fact that Naïve Bayes can be viewed as a special, simplified subclass of Bayesian networks where all features are conditionally independent of each other given the class node. This relationship underscores that Naïve Bayes offers computational simplicity at the expense of modeling complex dependencies.

The process of developing a Bayesian network model involves several systematic steps. Initially, it requires domain knowledge to identify the relevant variables and understand their relationships. Subsequently, nodes are defined within the network, representing these variables, and the relationships between them are established by determining the probabilistic dependencies. This often involves designing a directed acyclic graph (DAG), where arrows denote causal or influential connections. Once the structure is set, the next critical step is parameter learning, which involves estimating the probabilities associated with each node—this is typically done using statistical methods on available data. The final phase involves validating the network through techniques like cross-validation or sensitivity analysis to ensure its accuracy and reliability. Developing Bayesian networks, therefore, combines domain expertise with statistical learning to build a model capable of reasoning under uncertainty and making inferences (Koller & Friedman, 2009).

References

• Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
• Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
• Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.