Summary, Main Concepts, and Takeaways
Recap what was covered in this chapter and examine the key takeaways.
We'll cover the following...
Summary of concepts
First, let's remember some common concepts that will be highlighted throughout this lesson.
Descriptive graph: A descriptive graph is a visual representation of a set of data or relationships between variables. It can be used to identify trends, patterns, and relationships in the data.
Bayesian network: A Bayesian network is a graphical model that represents the relationships between random variables and their probabilities. It uses conditional probability distributions to model the dependencies between variables.
Random variables: A random variable is a variable whose value is subject to randomness or uncertainty. In a Bayesian network, each node represents a random variable.
Conditional probability distribution: A conditional ...