An EliminatableClusterTree, i.e., a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes net.

A EliminatableClusterTree, i.e., a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes net.

A junction tree specialized to Gaussian factors, i.e., it is a cluster tree with Gaussian factors stored in each cluster.

A JunctionTree is a cluster tree, a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes Net.

Bayes tree.

In GTSAM a junction tree is an intermediate data structure in multifrontal variable elimination. Each node is a cluster of factors, along with a clique of variables that are eliminated all at once. In detail, every node k represents a clique (maximal fully connected subset) of an associated chordal graph, such as a chordal Bayes net resulting from elimination.

The difference with the BayesTree is that a JunctionTree stores factors, whereas a BayesTree stores conditionals, that are the product of eliminating the factors in the corresponding JunctionTree cliques.

The tree structure and elimination method are exactly analogous to the EliminationTree, except that in the JunctionTree, at each node multiple variables are eliminated at a time.

Template Parameters

CONDITIONAL	The type of the conditional densities, i.e. the type of node in the underlying Bayes chain, which could be a ConditionalProbabilityTable, a GaussianConditional, or a SymbolicConditional.
CLIQUE	The type of the clique data structure, defaults to BayesTreeClique, normally do not change this as it is only used when developing special versions of BayesTree, e.g. for ISAM2.

In GTSAM a junction tree is an intermediate data structure in multifrontal variable elimination. Each node is a cluster of factors, along with a clique of variables that are eliminated all at once. In detail, every node k represents a clique (maximal fully connected subset) of an associated chordal graph, such as a chordal Bayes net resulting from elimination.

The difference with the BayesTree is that a JunctionTree stores factors, whereas a BayesTree stores conditionals, that are the product of eliminating the factors in the corresponding JunctionTree cliques.

The tree structure and elimination method are exactly analagous to the EliminationTree, except that in the JunctionTree, at each node multiple variables are eliminated at a time.

It can be eliminated into a Gaussian Bayes tree with the same structure, which is essentially doing multifrontal sparse matrix factorization.