The junction tree algorithm is an exact inference method for probabilistic graphical models. It transforms a graph into a tree of clusters so that probabilities can be propagated efficiently to compute marginal distributions or answer probabilistic queries.
(Also Clique Tree.) A method used in machine learning to extract marginalization in general graphs. In essence, it entails performing belief propagation on a modified graph called a junction tree. The graph is called a tree because it branches into different sections of data; nodes of variables are the branches.