Probability can become hard to reason about when many variables interact. One variable affects another. Evidence changes belief. Dependencies start to form a network. That is where Probabilistic Graphical Models become useful. Core Idea A Probabilistic Graphical Model represents uncertainty with a graph. The nodes are random variables. The edges represent relationships between them. Instead of treating probability as a flat list of formulas, a PGM gives it structure. That structure makes complex uncertainty easier to reason about. The Key Structure A simple PGM view looks like this: Random Variables → Graph Structure → Probability Values → Inference More compactly: PGM = graph + probability + inference The graph shows how variables are connected. The probability values define how likely different states are. Inference uses both to answer questions under uncertainty.…