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Fast Parallel Exact Inference on Bayesian Networks: Poster

arXiv 2212.04241 ↗First posted 8 Dec 2022cs.DC cs.AIcited by 4

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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.