Junction tree algorithm

In essence, it entails performing belief propagation on a modified graph called a junction tree.

Multiple extensive classes of queries can be compiled at the same time into larger structures of data.

Inference algorithms gather new developments in the data and calculate it based on the new information provided.

[3] The Hugin algorithm takes fewer computations to find a solution compared to Shafer-Shenoy.

[7] The first step concerns only Bayesian networks, and is a procedure to turn a directed graph into an undirected one.

The last step is to apply belief propagation to the obtained junction tree.

[10] Usage: A junction tree graph is used to visualize the probabilities of the problem.

[11] A specific use could be found in auto encoders, which combine the graph and a passing network on a large scale automatically.