Artificial Intelligence Programming Practice Exam 2026 - Free AI Programming Practice Questions and Study Guide

A Bayesian network represents a collection of random variables and their dependencies, showcasing the probabilistic relationships among them. It consists of nodes, which represent the random variables, and directed edges, which illustrate the conditional dependencies. This structure allows for the representation of joint probability distributions efficiently.

In a Bayesian network, the nature of the dependencies is defined such that each variable is conditionally independent of its non-parents, which enables the use of Bayes' theorem for updating beliefs in the presence of new evidence. This is a powerful feature that allows for reasoning under uncertainty, making Bayesian networks widely used in various fields like statistics, machine learning, and artificial intelligence to model complex systems where the interactions among variables must be taken into account.

The other options do not capture the essence of what a Bayesian network is. Linear equations focus on deterministic relationships rather than probabilistic ones, a time-series analysis model deals specifically with data points indexed in time order rather than a general representation of variable dependencies, and a hierarchical classification system is primarily about categorizing data without necessarily capturing the probabilistic relationships that a Bayesian network does.