Constraint Processing in Lifted Probabilistic Inference
Constraint Processing in Lifted Probabilistic Inference
复制标题
提升概率推理中的约束处理
DOI：
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发表时间：
2009
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：
Jacek Kisynski;D. Poole
一阶概率模型结合了一阶逻辑的表征能力和图形模型。目前正在努力为一阶概率模型设计提升推理算法。我们从约束处理的角度分析了提升推理，并通过这一观点分析和比较了现有的方法，揭示了它们的优点和局限性。我们的理论结果表明，约束处理方法的错误选择会导致计算复杂度的指数级增加。我们的实证检验证实了约束处理在提升推理中的重要性。这是提升推理中约束加工的第一个理论和实证研究。
First-order probabilistic models combine representational power of first-order logic with graphical models. There is an ongoing effort to design lifted inference algorithms for first-order probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through this viewpoint, we analyze and compare existing approaches and expose their advantages and limitations. Our theoretical results show that the wrong choice of constraint processing method can lead to exponential increase in computational complexity. Our empirical tests confirm the importance of constraint processing in lifted inference. This is the first theoretical and empirical study of constraint processing in lifted inference.