Probabilistic Graphical Models (PGMs) are frameworks that leverage graph theory to succinctly capture the complex dependencies among random variables in probabilistic systems. By using nodes to represent variables and edges to depict conditional dependencies, PGMs enable efficient computation and inference, making them invaluable in domains such as machine learning, natural language processing, and computer vision.