Distribution-Free Learning of Bayesian Network Structure
Abstract
We present an independence-based method for learning Bayesian network (BN) structure without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous domains. Even mixed continuous-categorical domains and structures containing vectorial variables can be handled. We address the problem by developing a non-parametric conditional independence test based on the so-called kernel dependence measure, which can be readily used by any existing independence-based BN structure learning algorithm. We demonstrate the structure learning of graphical models in continuous and mixed domains from real-world data without distributional assumptions. We also experimentally show that our test is a good alternative, in particular in case of small sample sizes, compared to existing tests, which can only be used in purely categorical or continuous domains.
Cite
Text
Sun. "Distribution-Free Learning of Bayesian Network Structure." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87481-2_28Markdown
[Sun. "Distribution-Free Learning of Bayesian Network Structure." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/sun2008ecmlpkdd-distributionfree/) doi:10.1007/978-3-540-87481-2_28BibTeX
@inproceedings{sun2008ecmlpkdd-distributionfree,
title = {{Distribution-Free Learning of Bayesian Network Structure}},
author = {Sun, Xiaohai},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {423-439},
doi = {10.1007/978-3-540-87481-2_28},
url = {https://mlanthology.org/ecmlpkdd/2008/sun2008ecmlpkdd-distributionfree/}
}