Guided structure learning of DAGs for count data
Methodology
2024-01-19 v2
Abstract
In this paper, we tackle structure learning of Directed Acyclic Graphs (DAGs), with the idea of exploiting available prior knowledge of the domain at hand to guide the search of the best structure. In particular, we assume to know the topological ordering of variables in addition to the given data. We study a new algorithm for learning the structure of DAGs, proving its theoretical consistence in the limit of infinite observations. Furthermore, we experimentally compare the proposed algorithm to a number of popular competitors, in order to study its behavior in finite samples.
Cite
@article{arxiv.2206.09754,
title = {Guided structure learning of DAGs for count data},
author = {Thi Kim Hue Nguyen and Monica Chiogna and Davide Risso and Erika Banzato},
journal= {arXiv preprint arXiv:2206.09754},
year = {2024}
}
Comments
arXiv admin note: text overlap with arXiv:1810.10854