Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models
Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models
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DOI：
10.24963/ijcai.2019/797
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Tahrima Rahman;Shasha Jin;Vibhav Gogate
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文献类型：
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作者：
Tahrima Rahman;Shasha Jin;Vibhav Gogate
最近，人们越来越关注从数据中学习允许多时间推断的概率模型，称为易处理的概率模型。虽然与难以处理的模型相比，它们的泛化能力较差，但它们通常在预测时产生更准确的估计。在本文中，我们试图进一步探索推广性能和推理精度之间的权衡，提出了一种新的，部分易处理的表示称为割集贝叶斯网络（CBN）。CBN的主要思想是将变量划分为两个子集X和Y，学习表示P（X）的（难处理的）贝叶斯网络和表示P（Y）的易处理的条件模型|X）。希望棘手的模型将有助于提高泛化能力，而易处理的模型，通过利用结合了精确推理和采样的Rao-Blackwellised采样，将有助于提高预测精度。要计算模型P（Y| X），我们引入了一种新的易处理的表示称为条件割集网络（CCN），其中所有的条件概率分布表示使用校准的分类器，分类器通常产生更高质量的概率估计比传统的分类器。我们通过严格的实验评估表明，CBN和CCN产生更准确的后验估计比他们的听话，以及棘手的同行。
Recently there has been growing interest in learning probabilistic models that admit poly-time inference called tractable probabilistic models from data. Although they generalize poorly as compared to intractable models, they often yield more accurate estimates at prediction time. In this paper, we seek to further explore this trade-off between generalization performance and inference accuracy by proposing a novel, partially tractable representation called cutset Bayesian networks (CBNs). The main idea in CBNs is to partition the variables into two subsets X and Y, learn a (intractable) Bayesian network that represents P(X) and a tractable conditional model that represents P(Y|X). The hope is that the intractable model will help improve generalization while the tractable model, by leveraging Rao-Blackwellised sampling which combines exact inference and sampling, will help improve the prediction accuracy. To compactly model P(Y|X), we introduce a novel tractable representation called conditional cutset networks (CCNs) in which all conditional probability distributions are represented using calibrated classifiers—classifiers which typically yield higher quality probability estimates than conventional classifiers. We show via a rigorous experimental evaluation that CBNs and CCNs yield more accurate posterior estimates than their tractable as well as intractable counterparts.