CTA
Package | trilearn |
Version | 2.0.5 |
Language | Python |
Docs | |
Paper | :footcite:t:`olsson2022sequential` |
Graph type | DG |
Docker | bpimages/trilearn:2.0.5 |
Module | trilearn_cta |
Description
This is the Christmas tree algorithm (CTA) for generating decomposable graph implemented in the PyPi package trilearn.
This algortihm generates a decomposable graph iteratively one node at a time by making use of the junction tree represenantation.
alpha
and beta
are sparsity parameters, where
beta
is the probabilty of creatinging a new node in each iteration isolated.
alpha
is the probabilty of connecting the new node in each iteration to another clique in a random tree traversal, given that is is not isolated (which is controlled by beta
).
In summary, high values of alpha
, beta
give denser graphs.
Example
[
{
"id": "trilearn_cta",
"order": 50,
"alpha": 0.5,
"beta": 0.7
}
]
.. footbibliography::