PGMs (Probabilistic Graphical Models)

Probabilistic Graphical Models (PGMs) are visual tools that help us understand and analyze complex relationships between variables involving uncertainty. They use graphs—nodes representing variables and edges showing their dependencies—to organize and simplify the calculation of probabilities. PGMs make it easier to infer unknown information, predict outcomes, and identify how different factors influence each other. They are widely used in fields like artificial intelligence, medical diagnosis, and machine learning to model real-world systems where uncertainty and interconnected data are common, providing a structured way to reason about complex probabilistic questions.