Fast Parallel Exact Inference on Bayesian Networks: Poster
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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.
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.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2024Fast-PGM: Fast Probabilistic Graphical Model Learning and Inferencecited by 2
- 2024FastBO: Fast HPO and NAS with Adaptive Fidelity Identificationcited by 1
- 2023Interpretable Traffic Event Analysis with Bayesian Networkscited by 0
- 2026Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertaintycited by 0
- 2026Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty12 Oct 2026
- 2024FastBO: Fast HPO and NAS with Adaptive Fidelity Identification1 Sep 2024
- 2024Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference24 May 2024
- 2023Interpretable Traffic Event Analysis with Bayesian Networks10 Oct 2023