What graph is used to show probabilistic relationships between different variables?

A Bayesian Network is specifically designed to represent probabilistic relationships among a set of variables. In a Bayesian Network, variables are depicted as nodes, and the relationships between them are shown as directed edges or arrows. Each edge signifies a probabilistic dependency, indicating how the state of one variable may influence the state of another.

This type of network allows you to perform inference and update beliefs based on new evidence, making it a powerful tool in probabilistic reasoning and decision-making processes. Each node in the network incorporates probability distributions that quantify the relationship of that node with its parent nodes, thus enabling the calculation of joint probabilities across the network.

While Directed Acyclic Graphs (DAGs) are a broader category of graph structures that do not allow cycles and can be used in various contexts, including Bayesian Networks, they alone do not explicitly represent probabilistic relationships unless accompanied by the relevant probability distributions. Knowledge Graphs tend to focus more on relationships between entities and information rather than probabilistic modeling, and Symbolic Reasoning is related to formal logic and does not typically involve probabilistic elements. Thus, the choice of a Bayesian Network accurately captures the essence of representing probabilistic relationships among variables.