Bayesian Network Learning with Discrete Case-Control Data
Abstract
We address the problem of learning Bayesian networks from discrete, unmatched case- control data using specialized conditional in- dependence tests. Those tests can also be used for learning other types of graphical models or for feature selection. We also propose a post-processing method that can be applied in conjunction with any Bayesian network learning algorithm. In simulations we show that our methods are able to deal with selection bias from case-control data.
Cite
Text
Borboudakis and Tsamardinos. "Bayesian Network Learning with Discrete Case-Control Data." Conference on Uncertainty in Artificial Intelligence, 2015.Markdown
[Borboudakis and Tsamardinos. "Bayesian Network Learning with Discrete Case-Control Data." Conference on Uncertainty in Artificial Intelligence, 2015.](https://mlanthology.org/uai/2015/borboudakis2015uai-bayesian/)BibTeX
@inproceedings{borboudakis2015uai-bayesian,
title = {{Bayesian Network Learning with Discrete Case-Control Data}},
author = {Borboudakis, Giorgos and Tsamardinos, Ioannis},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2015},
pages = {151-160},
url = {https://mlanthology.org/uai/2015/borboudakis2015uai-bayesian/}
}