ClusterTreeCompile {BayesNetBP} | R Documentation |

## 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 |

`node.class` |
a named |

### 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

### Examples

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

*BayesNetBP*version 1.6.1 Index]