Probabilistic Inference in Hybrid Domains by Weighted Model Integration
Probabilistic Inference in Hybrid Domains by Weighted Model Integration
复制标题
通过加权模型集成进行混合域中的概率推理
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Guy Van den Broeck
中科院分区：
文献类型：
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作者：
Vaishak Belle;A. Passerini;Guy Van den Broeck
命题知识库上的加权模型计数(WMC)是一种有效和通用的概率推理方法，适用于各种形式的概率推理，包括贝叶斯和马尔可夫网络。然而，WMC的一个固有局限性是它只允许对离散概率分布进行推断。在本文中，我们介绍了WMC的一种严格推广，称为加权模型集成，它基于对布尔约束和算术约束及其组合的注释。该方法可以捕获离散、连续和混合马尔可夫网络。然后，我们考虑语言片段的参数学习任务。实证评估证明了该建议的适用性和前景。
Weighted model counting (WMC) on a propositional knowledge base is an effective and general approach to probabilistic inference in a variety of formalisms, including Bayesian and Markov Networks. However, an inherent limitation of WMC is that it only admits the inference of discrete probability distributions. In this paper, we introduce a strict generalization of WMC called weighted model integration that is based on annotating Boolean and arithmetic constraints, and combinations thereof. This methodology is shown to capture discrete, continuous and hybrid Markov networks. We then consider the task of parameter learning for a fragment of the language. An empirical evaluation demonstrates the applicability and promise of the proposal.