超树分解在铂族金属中的应用
Hypertree Decompositions Revisited for PGMs
摘要：
我们重新审视经典问题的精确推理概率图模型（PGMs）。我们的算法是基于最近的最坏情况下的最优数据库连接算法，它可以渐近快于传统的数据处理方法。我们提出了这些新算法通过JoinInfer，一个新的精确推理引擎的第一个实证评估。我们凭经验探索的数据属性，我们的引擎可以预期优于传统的推理引擎精炼当前的理论概念。此外，JoinInfer在一些标准基准数据集上的性能优于现有的最先进的推理引擎（ACE，IJGP和libDAI），高达630倍。最后，我们提出了一个有前途的数据驱动的启发式，扩展JoinInfer自动调整其参数和/或切换到传统的推理算法。
英文摘要：
We revisit the classical problem of exact inference on probabilistic graphical models (PGMs). Our algorithm is based on recent worst-case optimal database join algorithms, which can be asymptotically faster than traditional data processing methods. We present the first empirical evaluation of these new algorithms via JoinInfer, a new exact inference engine. We empirically explore the properties of the data for which our engine can be expected to outperform traditional inference engines refining current theoretical notions. Further, JoinInfer outperforms existing state-of-the-art inference engines (ACE, IJGP and libDAI) on some standard benchmark datasets by up to a factor of 630x. Finally, we propose a promising data-driven heuristic that extends JoinInfer to automatically tailor its parameters and/or switch to the traditional inference algorithms.
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