Knowledge Compilation for Lifted Probabilistic Inference: Compiling to a Low-Level Language
Knowledge Compilation for Lifted Probabilistic Inference: Compiling to a Low-Level Language
复制标题
用于提升概率推理的知识编译：编译为低级语言
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. Poole
中科院分区：
文献类型：
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作者：
Seyed Mehran Kazemi;D. Poole
基于一阶知识编译的算法是目前提升推理的最新研究成果。这些算法通常将概率关系模型编译成中间数据结构，并使用它来回答许多推理查询。在本文中，我们提出将概率关系模型直接编译为低级目标（例如，C或C++）程序，而不是中间数据结构，并利用程序编译的进步。我们的实验代表数量级的加速比现有的方法。
Algorithms based on first-order knowledge compilation are currently the state-of-the-art for lifted inference. These algorithms typically compile a probabilistic relational model into an intermediate data structure and use it to answer many inference queries. In this paper, we propose compiling a probabilistic relational model directly into a low-level target (e.g., C or C++) program instead of an intermediate data structure and taking advantage of advances in program compilation. Our experiments represent orders of magnitude speedup compared to existing approaches.