This Week We Discuss The Overall Process Of Developing New S ✓ Solved

This Week We Discuss The Overall Process Of Developing New Softwa

This week we discuss the overall process of developing new software, focusing on the steps involved from initial planning to deployment and maintenance. The software development process typically includes requirement analysis, system design, implementation, testing, deployment, and ongoing maintenance. These phases ensure that the software meets user needs and functions correctly throughout its lifecycle.

In addition to understanding the development process, it is important to recognize the differences between software development methods and the process itself. Software development methods are systematic approaches or frameworks that guide how the development process is carried out. Examples include Agile, Waterfall, Scrum, and DevOps. These methods provide structured procedures, tools, and techniques for managing projects, coordinating teams, and ensuring quality. Therefore, while the process describes what steps are taken, methods specify how those steps are executed, often emphasizing different philosophies like flexibility, speed, or quality.

Understanding the Relationship Between Naïve Bayes and Bayesian Networks and Their Development

Naïve Bayes classifiers and Bayesian networks are both probabilistic graphical models based on Bayes’ theorem, but they differ in complexity and structure. Naïve Bayes is a simplified model that assumes features are conditionally independent given the class label, which makes it computationally efficient and suitable for many classification tasks (Sharda, Delen, & Turban, 2020). In contrast, Bayesian networks are more general graphical models that represent a set of variables and their conditional dependencies through a directed acyclic graph, capturing more complex relationships among variables.

The development process of a Bayesian network involves several key steps. First, defining the variables and their potential states based on domain knowledge or data analysis is essential. Second, constructing the network structure, which includes identifying the dependencies between variables, typically involves expert input or structure learning algorithms. Third, parameter learning involves estimating the conditional probability distributions for each node, given its parent nodes, often using statistical methods or data. Finally, the model needs to be validated and tested to ensure it accurately reflects the underlying domain and supports decision-making processes (Koller & Friedman, 2009). This iterative process can be enhanced by using data-driven techniques and domain expertise to optimize the network’s structure and parameters.

In summary, the primary relationship between Naïve Bayes and Bayesian networks lies in their shared foundation on Bayesian principles, with Naïve Bayes representing a simplified, independence-assuming model, and Bayesian networks providing a flexible framework for modeling complex interdependencies among multiple variables.

References

• Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
• Sharda, R., Delen, D., & Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. Pearson Education.