Probabilistic Graphical Models (PGMs) are a class of statistical models that use graphs to represent the relationships among a set of random variables. They provide a visual and mathematical framework for modeling complex distributions and enable reasoning under uncertainty. PGMs can be categorized into directed graphical models, such as Bayesian networks, and undirected graphical models, such as Markov random fields. These models are widely used in various AI applications, including machine learning, natural language processing, and computer vision, as they facilitate inference and learning in high-dimensional spaces while capturing the dependencies among variables.