New D-Separation Identification Results for Learning Continuous Latent Variable Models
Abstract
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hidden variables cannot be directly observed, one has to rely on alternative methods to identify the d-separations that define the graphical structure. This paper describes new distribution-free techniques for identifying d-separations in continuous latent variable models when non-linear dependencies are allowed among hidden variables.
Cite
Text
de Andrade e Silva and Scheines. "New D-Separation Identification Results for Learning Continuous Latent Variable Models." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102453Markdown
[de Andrade e Silva and Scheines. "New D-Separation Identification Results for Learning Continuous Latent Variable Models." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/deandradeesilva2005icml-new/) doi:10.1145/1102351.1102453BibTeX
@inproceedings{deandradeesilva2005icml-new,
title = {{New D-Separation Identification Results for Learning Continuous Latent Variable Models}},
author = {de Andrade e Silva, Ricardo Bezerra and Scheines, Richard},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {808-815},
doi = {10.1145/1102351.1102453},
url = {https://mlanthology.org/icml/2005/deandradeesilva2005icml-new/}
}