# Fast Parallel Exact Inference on Bayesian Networks: Poster

arXiv: https://arxiv.org/abs/2212.04241 · HTML: https://mlsanity.com/paper/2212.04241/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 5 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Built on by (most cited first, 4 of 4)

- [Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference](https://mlsanity.com/paper/2405.15605/) (2024)
- [FastBO: Fast HPO and NAS with Adaptive Fidelity Identification](https://mlsanity.com/paper/2409.00584/) (2024)
- [Interpretable Traffic Event Analysis with Bayesian Networks](https://mlsanity.com/paper/2310.06713/) (2023)
- [Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty](https://mlsanity.com/paper/2610.16902/) (2026)

## Abstract (arXiv)

Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact inference solution named Fast-BNI on multi-core CPUs. Fast-BNI enhances the efficiency of exact inference through hybrid parallelism that tightly integrates coarse- and fine-grained parallelism. We also propose techniques to further simplify the bottleneck operations of BN exact inference. Fast-BNI source code is freely available at https://github.com/jjiantong/FastBN.