Junction tree algorithm

<p>The junction tree algorithm (also known as 'Clique Tree')  is a method used in <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> to extract <a href="/facts/Marginal_distribution/U9XBWAd1">marginalization</a> in general <a href="/facts/Graph_(discrete_mathematics)/kw3eIBUe">graphs</a>.  In essence, it entails performing <a href="/facts/Belief_propagation/TRY5LVbP">belief propagation</a> on a modified graph called a <a href="/facts/Junction_tree/p31V5rM2">junction tree</a>. The graph is called a tree because it branches into different sections of data; <a href="/facts/Vertex_(graph_theory)/WhlWmL3o">nodes</a> of variables are the branches. The basic premise is to eliminate <a href="/facts/Cycle_(graph_theory)/Is44edrv">cycles</a> by clustering them into single nodes. Multiple extensive classes of queries can be compiled at the same time into larger structures of data. There are different <a href="/facts/Algorithm/fnl5NmRt">algorithms</a> to meet specific needs and for what needs to be calculated. <a href="/facts/Inference_network/Ry31vq6T">Inference algorithms</a> gather new developments in the data and calculate it based on the new information provided.
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Junction tree algorithm open-in-new

Junction tree algorithm