A graphical model is a way to represent complex relationships among variables using graphs. In these models, nodes represent variables, while edges (lines connecting the nodes) indicate the relationships or dependencies between them. This visual representation helps in understanding how different factors interact and influence one another. There are two main types of graphical models: Bayesian networks and Markov random fields. Bayesian networks use directed edges to show causal relationships, while Markov random fields use undirected edges to represent dependencies without implying causation. Both types are widely used in fields like statistics, machine learning, and artificial intelligence.