Summary, Main Concepts, and Takeaways

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 probability distribution is a probability distribution that describes the likelihood of an event, given the occurrence of a related event. It is used to represent the dependencies between variables in a Bayesian network.

Bayesian networks: BNs use conditional probabilities to describe events. Any belief about the uncertainty of an event or hypothesis $H$ is assumed provisional. This is called prior probability or $P(H)$ ...