论文标题
提起的混合变量推断
Lifted Hybrid Variational Inference
论文作者
论文摘要
已经提出了各种提升的推理算法，这些推理算法利用模型对称性来降低计算成本，以在概率关系模型中呈现推理。大多数现有的提起的推理算法仅在离散域或具有限制潜在功能的连续域上运行，例如高斯。我们研究了一种适用于混合域的两种近似抬高的变分方法，并且表现力足以捕获多模式。我们证明，即使在存在大量连续证据的情况下，提出的变分方法既可以扩展，又可以利用近似模型对称性。我们证明，我们的方法与在各种环境中的现有基于消息的方法进行了有利的比较。最后，我们为伯特近似提供了足够的条件，可以在边缘多层室上产生非平凡的估计值。
A variety of lifted inference algorithms, which exploit model symmetry to reduce computational cost, have been proposed to render inference tractable in probabilistic relational models. Most existing lifted inference algorithms operate only over discrete domains or continuous domains with restricted potential functions, e.g., Gaussian. We investigate two approximate lifted variational approaches that are applicable to hybrid domains and expressive enough to capture multi-modality. We demonstrate that the proposed variational methods are both scalable and can take advantage of approximate model symmetries, even in the presence of a large amount of continuous evidence. We demonstrate that our approach compares favorably against existing message-passing based approaches in a variety of settings. Finally, we present a sufficient condition for the Bethe approximation to yield a non-trivial estimate over the marginal polytope.
