# Structural Dimension Reduction in Bayesian Networks

arXiv: https://arxiv.org/abs/2601.08236 · HTML: https://mlsanity.com/paper/2601.08236/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future](https://mlsanity.com/paper/2002.08627/) (2020)
- [Computational Advantages of Relevance Reasoning in Bayesian Belief Networks](https://mlsanity.com/paper/1302.1558/) (2013)

## Built on by (most cited first, 1 of 1)

- [Decomposition for Bayesian Networks: Local and Parallel Inference](https://mlsanity.com/paper/2607.04650/) (2026)

## Abstract (arXiv)

This work introduces a novel technique, named structural dimension reduction, to collapse a Bayesian network onto a minimum and localized one while ensuring that probabilistic inferences between the original and reduced networks remain consistent. To this end, we propose a new combinatorial structure in directed acyclic graphs called the directed convex hull, which has turned out to be equivalent to their minimum localized Bayesian networks. An efficient polynomial-time algorithm is devised to identify them by determining the unique directed convex hulls containing the variables of interest from the original networks. Experiments demonstrate that the proposed technique has high dimension reduction capability in real networks, and the efficiency of probabilistic inference based on directed convex hulls can be significantly improved compared with traditional methods such as variable elimination and belief propagation algorithms. The code of this study is open at hrefhttps://github.com/Balance-H/Algorithmshttps://github.com/Balance-H/Algorithms and the proofs of the results in the main body are postponed to the appendix.