ClusterTreeCompile: Compile the cluster tree

In BayesNetBP: Bayesian Network Belief Propagation

Compile the cluster tree

Description

Get the cluster sets and strong semi-elimination tree from the Bayesian network

Usage

ClusterTreeCompile(dag, node.class)

Arguments

dag	a graphNEL object of the Bayesian network
node.class	a named vector of logical values, TRUE if node is discrete, FASLE if otherwise

Details

This function forms the cluster sets and the semi-elimination tree graph from the Bayesian network. The procedures include acquiring the elimination order, moralization, triangulation, obtaining cluster sets, forming strong elimination tree and strong semi-elimination tree. The cluster sets and the semi-elimination tree are required to initialize the cluster tree.

Value

tree.graph

a graphNEL object of semi-elimination tree.

dag

a graphNEL object of original Bayesian network.

cluster.sets

a list of members of each cluster.

node.class

a named vector of logical values, TRUE if node is discrete, FASLE if otherwise

elimination.order

a vector of node names sorted by the elimination order.

Author(s)

Han Yu

References

Cowell, R. G. (2005). Local propagation in conditional Gaussian Bayesian networks. Journal of Machine Learning Research, 6(Sep), 1517-1550.

Yu H, Moharil J, Blair RH (2020). BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks. Journal of Statistical Software, 94(3), 1-31. <doi:10.18637/jss.v094.i03>.

See Also

ElimTreeInitialize

Examples

data(liver)
cst <- ClusterTreeCompile(dag=liver$dag, node.class=liver$node.class)

BayesNetBP documentation built on May 9, 2022, 1:05 a.m.