The junction tree algorithm performs exact inference in a finite probabilistic graphical model. First moralize a Bayesian network if necessary; then apply triangulation of an undirected graph, organize its maximal cliques into a junction tree, and assign each original factor to one containing clique. Inward and outward junction-tree sum-product messages compute the normalizing constant and marginal distributions. Its cost depends exponentially on the largest clique's number of variables, rather than necessarily on the total number of variables.
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The Junction Tree Algorithm is a method used in probabilistic graphical models, notably in Bayesian networks and Markov networks, to perform exact inference. The algorithm is designed to compute the marginal probabilities of a subset of variables given some evidence. It operates by transforming a graphical model into a junction tree, which is a specific type of data structure that facilitates efficient computation. ### Key Concepts 1. **Graphical Models**: These are representations of the structure of probability distributions over a set of random variables.