@misc{10481/102707,
year = {2025},
month = {1},
url = {https://hdl.handle.net/10481/102707},
abstract = {This study presents a novel variational framework for structural learning in Bayesian networks
(BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to large
graphs with many variables. Traditional approaches, such as MCMC and stochastic search, often encounter
computational barriers due to the super-exponential growth of the Directed Acyclic Graph (DAG) space.
Our method introduces a scalable alternative by leveraging a factorized variational family to approximate the
posterior distribution over DAG structures, enabling efficient computation of Bayesian scores and predictive
posterior inference. Unlike previous methods, which are constrained by high computational costs or domainspecific
limitations, this approach achieves tractability through mean-field variational inference and tractable
updating equations, allowing application to significantly larger datasets. Empirical results on benchmark
datasets demonstrate that the proposed framework consistently outperforms state-of-the-art methods in terms
of scalability and predictive accuracy while maintaining robustness across diverse scenarios. This work
represents a key step towards scalable Bayesian structural learning and opens avenues for future research to
refine the variational approximation and incorporate advanced parallelization techniques.},
organization = {PID2022-139293NB-C33, funded by Ministerio de Ciencia, Inovación y Universidades
(MICIU)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and the European Regional Development Fund (ERDF) of the
European Union.},
publisher = {IEEE},
keywords = {Bayesian networks},
keywords = {probabilistic graphical models},
keywords = {structural learning},
title = {Toward Variational Structural Learning of Bayesian Networks},
doi = {10.1109/ACCESS.2025.3533878},
author = {Masegosa, Andrés R. and Gómez Olmedo, Manuel},
}