Learning Linear Bayesian Networks with Latent Variables
Abstract
This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established under a novel graphical constraint. The constraint concerns the expansion properties of the underlying directed acyclic graph (DAG) between observed and unobserved variables in the network, and it is satisfied by many natural families of DAGs that include multi-level DAGs, DAGs with effective depth one, as well as certain families of polytrees.
Cite
Text
Anandkumar et al. "Learning Linear Bayesian Networks with Latent Variables." International Conference on Machine Learning, 2013.Markdown
[Anandkumar et al. "Learning Linear Bayesian Networks with Latent Variables." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/anandkumar2013icml-learning/)BibTeX
@inproceedings{anandkumar2013icml-learning,
title = {{Learning Linear Bayesian Networks with Latent Variables}},
author = {Anandkumar, Animashree and Hsu, Daniel and Javanmard, Adel and Kakade, Sham},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {249-257},
volume = {28},
url = {https://mlanthology.org/icml/2013/anandkumar2013icml-learning/}
}