https://github.com/hayesall/unidags

🦄 Uniformly distributed samples of directed acyclic graphs for random Bayesian Networks
https://github.com/hayesall/unidags

bayesian-networks

Last synced: 11 months ago
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🦄 Uniformly distributed samples of directed acyclic graphs for random Bayesian Networks

• Host: GitHub
• URL: https://github.com/hayesall/unidags
• Owner: hayesall
• Created: 2021-02-23T15:34:38.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2021-02-23T15:34:59.000Z (over 5 years ago)
• Last Synced: 2025-07-09T17:05:56.126Z (11 months ago)
• Topics: bayesian-networks
• Language: Python
• Homepage:
• Size: 48.8 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# 🦄 UniDAGs

> "*Uniformly distributed samples of directed acyclic graphs for random Bayesian Networks.*"

## Overview

Based on Ide and Cozman 2002 "*Random Generation of Bayesian Networks*." Which presented algorithms for generating random Bayesian network structures in the form of:

Trees:

![](docs/tree1.png)

Directed Acyclic Graphs:

![](docs/graph1.png)

... and Polytrees:

![](docs/polytree1.png)

## Notes and Warnings

⚠️ "Algorithm 2" for random polytree generation does not match the

algorithm presented in the paper. Using it currently prints a warning to the console.

## Getting Started

This only requires `networkx>=2.5` currently:

```console

pip install -r requirements.txt

```

Methods for generating structures can be imported:

```python

>>> from unidag.generate import random_tree_graph

>>> from unidag.generate import multi_dag

>>> from unidag.generate import polytree

```

A main module provides an interface to choose (1) number of nodes, (2) number of

iterations for the Markov chain, and (3) an algorithm `{tree,polytree,graph}`.

For options and further instructions, pass a `--help` flag:

```console

python unidag/generate.py --help

```

## Acknowledgements

### BibTex

```bibtex

@inproceedings{ide2002randomgeneration,

  author="Ide, Jaime S. and Cozman, Fabio G.",

  editor="Bittencourt, Guilherme and Ramalho, Geber L.",

  title="Random Generation of Bayesian Networks",

  booktitle="Advances in Artificial Intelligence",

  year="2002",

  publisher="Springer Berlin Heidelberg",

  address="Berlin, Heidelberg",

  pages="366--376",

  isbn="978-3-540-36127-5"

}

```