Decomposition for Bayesian Networks: Local and Parallel Inference
Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size. We propose a decomposition framework based on directed convex subgraphs and introduce a minimal d-decomposition tree. Together, they provide a principled alternative to classical junction-tree constructions. The proposed framework represents the joint distribution by lower-dimensional sub-models that can be learned and stored separately. This decomposition reduces computational cost and naturally enables parallel computation. Based on a minimal d-decomposition tree, we further develop two parallel algorithms for parameter e...
| Authors | Pei Heng, Xinyi Hu, Yi Sun |
|---|---|
| Primary category | stat.ML |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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