Junction tree algorithm

The junction tree algorithm is 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 lead explains that the graph is called a tree because it branches into different sections of data, with nodes of variables as the branches, and that the basic premise is to eliminate cycles by clustering them into single nodes. Multiple extensive classes of queries can be compiled at the same time into larger structures of data.