Variational Inference in Mixed Probabilistic Submodular Models
Variational Inference in Mixed Probabilistic Submodular Models
复制标题
混合概率子模模型中的变分推理
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Andreas Krause
中科院分区：
文献类型：
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作者：
Josip Djolonga;Sebastian Tschiatschek;Andreas Krause
考虑了具有对数次模和对数超模高阶势的概率模型的变分推断问题。这些模型可以表示二进制变量上的任意分布，从而推广了常用的成对马尔可夫随机场和模型，只有对数超模势，有效的近似推理算法是已知的。虽然所考虑的模型中的推理是P-硬一般，我们提出了有效的近似算法，利用离散优化领域的最新进展。我们证明了我们的方法的有效性，在一个大的实验集，我们的模型允许推理的项目集的补充和替代品的偏好。
We consider the problem of variational inference in probabilistic models with both log-submodular and log-supermodular higher-order potentials. These models can represent arbitrary distributions over binary variables, and thus generalize the commonly used pairwise Markov random fields and models with log-supermodular potentials only, for which efficient approximate inference algorithms are known. While inference in the considered models is #P-hard in general, we present efficient approximate algorithms exploiting recent advances in the field of discrete optimization. We demonstrate the effectiveness of our approach in a large set of experiments, where our model allows reasoning about preferences over sets of items with complements and substitutes.