bnlearn_gs
Grow-shrink
Package |
|
Version |
4.8.3 |
Language |
|
Docs |
|
Paper |
Margaritis[1] |
Graph type |
|
Docker |
|
Module folder |
Description
The grow-shrink (GS) algorithm is based on the Markov blanket of the nodes in a DAG. For a specific node, the Markov blanket it the set of nodes which conditioning upon renders it conditionally independent from all other variables Margaritis[1]. It is a constraint-based method which estimates the Markov blanket of a node in a two-stage forward-backward proce- dure using conditional independence tests. The Markov blankets are used to first estimate an undirected graph and then estimate a DAG in a four-step procedure.
Example JSON
[
{
"id": "gs-mi",
"alpha": [
0.01,
0.05,
0.1,
0.2
],
"test": "mi",
"B": null,
"maxsx": null,
"debug": false,
"undirected": false,
"timeout": null
},
{
"id": "gs-zf",
"alpha": [
0.01,
0.05
],
"test": "zf",
"B": null,
"maxsx": null,
"debug": false,
"undirected": false,
"timeout": null
}
]