Graphical models are a way to represent complex relationships among variables using graphs. In these models, nodes represent random variables, while edges (or lines) indicate the dependencies between them. This visual representation helps in understanding how different variables interact and influence each other, making it easier to analyze and predict outcomes. There are two main types of graphical models: Bayesian networks and Markov random fields. Bayesian networks are directed acyclic graphs that show probabilistic relationships, while Markov random fields are undirected graphs that represent joint distributions. Both types are widely used in fields like machine learning, statistics, and artificial intelligence for tasks such as inference and decision-making.